Codemotion, Rome, April 14th, 2018
Algorithms & Machine Learning
And Then There Are Algorithms
Danilo Poccia
Evangelist, Serverless
[email protected]
@danilop
danilop
Letter from Ada Lovelace to Charles Babbage 1843
In this letter, Lovelace suggests an example of a calculation
which “may be worked out by the engine without having been
worked out by human head and hands first”.
Science Museum Group Collection
© The Board of Trustees of the Science Museum
Diagram of an algorithm for the Analytical Engine for the computation of Bernoulli numbers, from Sketch of
The Analytical Engine Invented by Charles Babbage by Luigi Menabrea with notes by Ada Lovelace
Muhammad ibn Musa al-Khwarizmi
(c. 780 – c. 850)
Why “Algorithm”?
What is an Algorithm?
https://commons.wikimedia.org/wiki/File:Euclid_flowchart.svg
By Somepics (Own work) [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons
A B
12 18
12 6
6 6
6 0
Euclid’s algorithm for the GCD
of two numbers
“You use code to tell a computer what to do.
Before you write code you need an algorithm.
An algorithm is a list of rules to follow
in order to solve a problem.”
BBC Bitesize
What is an Algorithm?
https://commons.wikimedia.org/wiki/File:Euclid_flowchart.svg
By Somepics (Own work) [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons
The Master Algorithm
“The future belongs to those who
understand at a very deep level how
to combine their unique expertise
with what algorithms do best.”
Pedro Domingos
The Five Tribes of Machine Learning
Tribe Origins Master Algorithm
Symbolists Logic, philosophy Inverse deduction
Connectionists Neuroscience Backpropagation
Evolutionaries
Evolutionary
biology
Genetic
programming
Bayesians Statistics
Probabilistic
inference
Analogizers Psychology Kernel machines
Linear Learner
Regression
Estimate a real valued function
Binary Classification
Predict a 0/1 class
Supervised
Classification, Regression
Bike Sharing Prediction (Regression)
Date Time
Temperature
(Celsius)
Relative
Humidity
Rain (mm/h) Rented Bikes
2018-04-01 08:30 13 64 2 45
2018-04-01 11:30 18 57 0 156
2018-04-02 08:30 14 69 8 87
2018-04-02 11:30 17 73 12 34
… … … … … …
Bike Sharing Prediction (Regression)
Date Time
Temperature
(Celsius)
Relative
Humidity
Rain (mm/h) Rented Bikes
2018-04-01 08:30 13 64 2 45
2018-04-01 11:30 18 57 0 156
2018-04-02 08:30 14 69 8 87
2018-04-02 11:30 17 73 12 34
2018-04-14 16:30 23 56 0 ???
Date & Time
Bike Sharing Prediction (Regression)
Day of
the Year
Weekday
Public
Holiday
Time
(seconds)
Temperature
(Celsius)
Relative
Humidity
Rain
(mm/h)
Rented
Bikes
91 7 1 30600 13 64 2 45
91 7 1 41400 18 57 0 156
92 1 1 30600 14 69 8 87
92 1 1 41400 17 73 12 34
104 6 0 59400 23 56 0 ???
Date & Time (Feature Engineering)
Linear Learner
basis functions
basis functions can be nonlinear
Supervised
Classification, Regression
Minimizing the Error
you know the expected values
(use separate datasets for
training and validation)
this is always positive
(convex function)
Supervised
Objective Function
loss
function
regularization
term
measures
how predictive
our model is on
your data
measures the
complexity of
the model
Supervised
Stochastic Gradient Descent (SGD)
https://en.wikipedia.org/wiki/Himmelblau's_function
Global
Vs
Local
Minimum
Factorization Machines
• It is an extension of a linear model that is
designed to parsimoniously capture
interactions between features within high
dimensional sparse datasets
• Factorization machines are a good choice for
tasks such as click prediction and item
recommendation
• They are usually trained by stochastic gradient
descent (SGD), alternative least square (ALS),
or Markov chain Monte Carlo (MCMC)
Factorization Machines
Steffen Rendle
Department of Reasoning for Intelligence
The Institute of Scientiﬁc and Industrial Research
Osaka University, Japan
[email protected]
Abstract—In this paper, we introduce Factorization Machines
(FM) which are a new model class that combines the advantages
of Support Vector Machines (SVM) with factorization models.
Like SVMs, FMs are a general predictor working with any
real valued feature vector. In contrast to SVMs, FMs model all
interactions between variables using factorized parameters. Thus
they are able to estimate interactions even in problems with huge
sparsity (like recommender systems) where SVMs fail. We show
that the model equation of FMs can be calculated in linear time
and thus FMs can be optimized directly. So unlike nonlinear
SVMs, a transformation in the dual form is not necessary and
the model parameters can be estimated directly without the need
of any support vector in the solution. We show the relationship
to SVMs and the advantages of FMs for parameter estimation
in sparse settings.
On the other hand there are many different factorization mod-
els like matrix factorization, parallel factor analysis or specialized
models like SVD++, PITF or FPMC. The drawback of these
models is that they are not applicable for general prediction tasks
but work only with special input data. Furthermore their model
equations and optimization algorithms are derived individually
for each task. We show that FMs can mimic these models just
by specifying the input data (i.e. the feature vectors). This makes
FMs easily applicable even for users without expert knowledge
in factorization models.
Index Terms—factorization machine; sparse data; tensor fac-
torization; support vector machine
I. INTRODUCTION
Support Vector Machines are one of the most popular
predictors in machine learning and data mining. Nevertheless
in settings like collaborative ﬁltering, SVMs play no important
role and the best models are either direct applications of
standard matrix/ tensor factorization models like PARAFAC
[1] or specialized models using factorized parameters [2], [3],
[4]. In this paper, we show that the only reason why standard
SVM predictors are not successful in these tasks is that they
cannot learn reliable parameters (‘hyperplanes’) in complex
(non-linear) kernel spaces under very sparse data. On the other
hand, the drawback of tensor factorization models and even
more for specialized factorization models is that (1) they are
not applicable to standard prediction data (e.g. a real valued
feature vector in Rn.) and (2) that specialized models are
usually derived individually for a speciﬁc task requiring effort
in modelling and design of a learning algorithm.
In this paper, we introduce a new predictor, the Factor-
ization Machine (FM), that is a general predictor like SVMs
but is also able to estimate reliable parameters under very
high sparsity. The factorization machine models all nested
variable interactions (comparable to a polynomial kernel in
SVM), but uses a factorized parametrization instead of a
dense parametrization like in SVMs. We show that the model
equation of FMs can be computed in linear time and that it
depends only on a linear number of parameters. This allows
direct optimization and storage of model parameters without
the need of storing any training data (e.g. support vectors) for
prediction. In contrast to this, non-linear SVMs are usually
optimized in the dual form and computing a prediction (the
model equation) depends on parts of the training data (the
support vectors). We also show that FMs subsume many of
the most successful approaches for the task of collaborative
ﬁltering including biased MF, SVD++ [2], PITF [3] and FPMC
[4].
In total, the advantages of our proposed FM are:
1) FMs allow parameter estimation under very sparse data
where SVMs fail.
2) FMs have linear complexity, can be optimized in the
primal and do not rely on support vectors like SVMs.
We show that FMs scale to large datasets like Netﬂix
with 100 millions of training instances.
3) FMs are a general predictor that can work with any real
valued feature vector. In contrast to this, other state-of-
the-art factorization models work only on very restricted
input data. We will show that just by deﬁning the feature
vectors of the input data, FMs can mimic state-of-the-art
models like biased MF, SVD++, PITF or FPMC.
II. PREDICTION UNDER SPARSITY
The most common prediction task is to estimate a function
y : Rn → T from a real valued feature vector x ∈ Rn to a
target domain T (e.g. T = R for regression or T = {+, −}
for classiﬁcation). In supervised settings, it is assumed that
there is a training dataset D = {(x(1), y(1)), (x(2), y(2)), . . .}
of examples for the target function y given. We also investigate
the ranking task where the function y with target T = R can
be used to score feature vectors x and sort them according to
their score. Scoring functions can be learned with pairwise
training data [5], where a feature tuple (x(A), x(B)) ∈ D
means that x(A) should be ranked higher than x(B). As the
pairwise ranking relation is antisymmetric, it is sufﬁcient to
use only positive training instances.
In this paper, we deal with problems where x is highly
sparse, i.e. almost all of the elements xi of a vector x are
zero. Let m(x) be the number of non-zero elements in the
2010
Supervised
Classification, regression
Factorization Machines
Source: data-artisans.com
2010
Supervised
Classification, regression
? ?
?
?
?
?
?
Factorization Machines
not in a Linear Learner
2010
Supervised
Classification, regression
Alternative
least square
(ALS)
features
Factorization Machines (k=4)
Movie
1
action
2
romantic
3
thriller
4
horror
Blade Runner 0.4 0.3 0.5 0.2
Notting Hill 0.2 0.8 0.1 0.01
Arrival 0.2 0.4 0.6 0.1
But you cannot really control how features are used!
2010
Supervised
Classification, regression
Intuitively, each “feature” describes a property of the “items”
Vectors ⇾ “Bearer of Information”
how much are
they related?
XGBoost
• Ensemble methods use multiple learning
algorithms to improve predictions
• Boosting: “Can a set of weak learners create a
single strong learner?”
• Gradient Boosting: using gradient descent over a
function space
• eXtreme Gradient Boosting
• https://github.com/dmlc/xgboost
• Supports regression, classification, ranking
and user defined objectives
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
University of Washington
[email protected]
Carlos Guestrin
University of Washington
[email protected]
ABSTRACT
Tree boosting is a highly e↵ective and widely used machine
learning method. In this paper, we describe a scalable end-
to-end tree boosting system called XGBoost, which is used
widely by data scientists to achieve state-of-the-art results
on many machine learning challenges. We propose a novel
sparsity-aware algorithm for sparse data and weighted quan-
tile sketch for approximate tree learning. More importantly,
we provide insights on cache access patterns, data compres-
sion and sharding to build a scalable tree boosting system.
By combining these insights, XGBoost scales beyond billions
of examples using far fewer resources than existing systems.
Keywords
Large-scale Machine Learning
1. INTRODUCTION
Machine learning and data-driven approaches are becom-
ing very important in many areas. Smart spam classiﬁers
protect our email by learning from massive amounts of spam
data and user feedback; advertising systems learn to match
the right ads with the right context; fraud detection systems
protect banks from malicious attackers; anomaly event de-
tection systems help experimental physicists to ﬁnd events
that lead to new physics. There are two important factors
that drive these successful applications: usage of e↵ective
(statistical) models that capture the complex data depen-
dencies and scalable learning systems that learn the model
of interest from large datasets.
Among the machine learning methods used in practice,
gradient tree boosting [10]1 is one technique that shines
in many applications. Tree boosting has been shown to
give state-of-the-art results on many standard classiﬁcation
benchmarks [16]. LambdaMART [5], a variant of tree boost-
ing for ranking, achieves state-of-the-art result for ranking
1Gradient tree boosting is also known as gradient boosting
machine (GBM) or gradient boosted regression tree (GBRT)
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classroom use is granted without fee provided that copies are not made or distributed
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For all other uses, contact the owner/author(s).
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DOI:
problems. Besides being used as a stand-alone predictor, it
is also incorporated into real-world production pipelines for
ad click through rate prediction [15]. Finally, it is the de-
facto choice of ensemble method and is used in challenges
such as the Netﬂix prize [3].
In this paper, we describe XGBoost, a scalable machine
learning system for tree boosting. The system is available as
an open source package2. The impact of the system has been
widely recognized in a number of machine learning and data
mining challenges. Take the challenges hosted by the ma-
chine learning competition site Kaggle for example. Among
the 29 challenge winning solutions 3 published at Kaggle’s
blog during 2015, 17 solutions used XGBoost. Among these
solutions, eight solely used XGBoost to train the model,
while most others combined XGBoost with neural nets in en-
sembles. For comparison, the second most popular method,
deep neural nets, was used in 11 solutions. The success
of the system was also witnessed in KDDCup 2015, where
XGBoost was used by every winning team in the top-10.
Moreover, the winning teams reported that ensemble meth-
ods outperform a well-conﬁgured XGBoost by only a small
amount [1].
These results demonstrate that our system gives state-of-
the-art results on a wide range of problems. Examples of
the problems in these winning solutions include: store sales
prediction; high energy physics event classiﬁcation; web text
classiﬁcation; customer behavior prediction; motion detec-
tion; ad click through rate prediction; malware classiﬁcation;
product categorization; hazard risk prediction; massive on-
line course dropout rate prediction. While domain depen-
dent data analysis and feature engineering play an important
role in these solutions, the fact that XGBoost is the consen-
sus choice of learner shows the impact and importance of
our system and tree boosting.
The most important factor behind the success of XGBoost
is its scalability in all scenarios. The system runs more than
ten times faster than existing popular solutions on a single
machine and scales to billions of examples in distributed or
memory-limited settings. The scalability of XGBoost is due
to several important systems and algorithmic optimizations.
These innovations include: a novel tree learning algorithm
is for handling
sparse data
; a theoretically justiﬁed weighted
quantile sketch procedure enables handling instance weights
in approximate tree learning. Parallel and distributed com-
puting makes learning faster which enables quicker model ex-
ploration. More importantly, XGBoost exploits out-of-core
2https://github.com/dmlc/xgboost
3Solutions come from of top-3 teams of each competitions.
arXiv:1603.02754v3 [cs.LG] 10 Jun 2016
2016
Supervised
Classification, regression
XGBoost
Classification And Regression Trees (CART)
2016
Supervised
Classification, regression
XGBoost
Tree Ensemble
2016
Supervised
Classification, regression
Image Classification
Deep Residual Learning for Image Recognition
Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun
Microsoft Research
{kahe, v-xiangz, v-shren, jiansun}@microsoft.com
Abstract
Deeper neural networks are more difﬁcult to train. We
present a residual learning framework to ease the training
of networks that are substantially deeper than those used
previously. We explicitly reformulate the layers as learn-
ing residual functions with reference to the layer inputs, in-
stead of learning unreferenced functions. We provide com-
prehensive empirical evidence showing that these residual
networks are easier to optimize, and can gain accuracy from
considerably increased depth. On the ImageNet dataset we
evaluate residual nets with a depth of up to 152 layers—8⇥
deeper than VGG nets [41] but still having lower complex-
ity. An ensemble of these residual nets achieves 3.57% error
on the ImageNet test set. This result won the 1st place on the
ILSVRC 2015 classiﬁcation task. We also present analysis
on CIFAR-10 with 100 and 1000 layers.
The depth of representations is of central importance
for many visual recognition tasks. Solely due to our ex-
tremely deep representations, we obtain a 28% relative im-
provement on the COCO object detection dataset. Deep
residual nets are foundations of our submissions to ILSVRC
& COCO 2015 competitions1, where we also won the 1st
places on the tasks of ImageNet detection, ImageNet local-
ization, COCO detection, and COCO segmentation.
1. Introduction
Deep convolutional neural networks [22, 21] have led
to a series of breakthroughs for image classiﬁcation [21,
50, 40]. Deep networks naturally integrate low/mid/high-
level features [50] and classiﬁers in an end-to-end multi-
layer fashion, and the “levels” of features can be enriched
by the number of stacked layers (depth). Recent evidence
[41, 44] reveals that network depth is of crucial importance,
and the leading results [41, 44, 13, 16] on the challenging
ImageNet dataset [36] all exploit “very deep” [41] models,
with a depth of sixteen [41] to thirty [16]. Many other non-
trivial visual recognition tasks [8, 12, 7, 32, 27] have also
1http://image-net.org/challenges/LSVRC/2015/ and
http://mscoco.org/dataset/#detections-challenge2015.
0 1 2 3 4 5 6
0
10
20
iter. (1e4)
training error (%)
0 1 2 3 4 5 6
0
10
20
iter. (1e4)
test error (%)
56-layer
20-layer
56-layer
20-layer
Figure 1. Training error (left) and test error (right) on CIFAR-10
with 20-layer and 56-layer “plain” networks. The deeper network
has higher training error, and thus test error. Similar phenomena
on ImageNet is presented in Fig. 4.
greatly beneﬁted from very deep models.
Driven by the signiﬁcance of depth, a question arises: Is
learning better networks as easy as stacking more layers?
An obstacle to answering this question was the notorious
problem of vanishing/exploding gradients [1, 9], which
hamper convergence from the beginning. This problem,
however, has been largely addressed by normalized initial-
ization [23, 9, 37, 13] and intermediate normalization layers
[16], which enable networks with tens of layers to start con-
verging for stochastic gradient descent (SGD) with back-
propagation [22].
When deeper networks are able to start converging, a
degradation problem has been exposed: with the network
depth increasing, accuracy gets saturated (which might be
unsurprising) and then degrades rapidly. Unexpectedly,
such degradation is not caused by overﬁtting, and adding
more layers to a suitably deep model leads to higher train-
ing error, as reported in [11, 42] and thoroughly veriﬁed by
our experiments. Fig. 1 shows a typical example.
The degradation (of training accuracy) indicates that not
all systems are similarly easy to optimize. Let us consider a
shallower architecture and its deeper counterpart that adds
more layers onto it. There exists a solution by construction
to the deeper model: the added layers are identity mapping,
and the other layers are copied from the learned shallower
model. The existence of this constructed solution indicates
that a deeper model should produce no higher training error
than its shallower counterpart. But experiments show that
our current solvers on hand are unable to ﬁnd solutions that
1
arXiv:1512.03385v1 [cs.CV] 10 Dec 2015
Densely Connected Convolutional Networks
Gao Huang⇤
Cornell University
[email protected]
Zhuang Liu⇤
Tsinghua University
[email protected]
Laurens van der Maaten
Facebook AI Research
[email protected]
Kilian Q. Weinberger
Cornell University
[email protected]
Abstract
Recent work has shown that convolutional networks can
be substantially deeper, more accurate, and efﬁcient to train
if they contain shorter connections between layers close to
the input and those close to the output. In this paper, we
embrace this observation and introduce the Dense Convo-
lutional Network (DenseNet), which connects each layer
to every other layer in a feed-forward fashion. Whereas
traditional convolutional networks with L layers have L
connections—one between each layer and its subsequent
layer—our network has L(L+1)
2
direct connections. For
each layer, the feature-maps of all preceding layers are
used as inputs, and its own feature-maps are used as inputs
into all subsequent layers. DenseNets have several com-
pelling advantages: they alleviate the vanishing-gradient
problem, strengthen feature propagation, encourage fea-
ture reuse, and substantially reduce the number of parame-
ters. We evaluate our proposed architecture on four highly
competitive object recognition benchmark tasks (CIFAR-10,
CIFAR-100, SVHN, and ImageNet). DenseNets obtain sig-
niﬁcant improvements over the state-of-the-art on most of
them, whilst requiring less computation to achieve high per-
formance. Code and pre-trained models are available at
https://github.com/liuzhuang13/DenseNet
.
1. Introduction
Convolutional neural networks (CNNs) have become
the dominant machine learning approach for visual object
recognition. Although they were originally introduced over
20 years ago [18], improvements in computer hardware and
network structure have enabled the training of truly deep
CNNs only recently. The original LeNet5 [19] consisted of
5 layers, VGG featured 19 [29], and only last year Highway
⇤Authors contributed equally
x0
x1
H1
x2
H2
H3
H4
x3
x4
Figure 1: A 5-layer dense block with a growth rate of k = 4.
Each layer takes all preceding feature-maps as input.
Networks [34] and Residual Networks (ResNets) [11] have
surpassed the 100-layer barrier.
As CNNs become increasingly deep, a new research
problem emerges: as information about the input or gra-
dient passes through many layers, it can vanish and “wash
out” by the time it reaches the end (or beginning) of the
network. Many recent publications address this or related
problems. ResNets [11] and Highway Networks [34] by-
pass signal from one layer to the next via identity connec-
tions. Stochastic depth [13] shortens ResNets by randomly
dropping layers during training to allow better information
and gradient ﬂow. FractalNets [17] repeatedly combine sev-
eral parallel layer sequences with different number of con-
volutional blocks to obtain a large nominal depth, while
maintaining many short paths in the network. Although
these different approaches vary in network topology and
training procedure, they all share a key characteristic: they
create short paths from early layers to later layers.
1
arXiv:1608.06993v5 [cs.CV] 28 Jan 2018
Inception Recurrent Convolutional Neural Network for Object Recognition
Md Zahangir Alom [email protected]
University of Dayton, Dayton, OH, USA
Mahmudul Hasan [email protected]
Comcast Labs, Washington, DC, USA
Chris Yakopcic [email protected]
University of Dayton, Dayton, OH, USA
Tarek M. Taha [email protected]
University of Dayton, Dayton, OH, USA
Abstract
Deep convolutional neural networks (DCNNs)
are an inﬂuential tool for solving various prob-
lems in the machine learning and computer vi-
sion ﬁelds. In this paper, we introduce a
new deep learning model called an Inception-
Recurrent Convolutional Neural Network (IR-
CNN), which utilizes the power of an incep-
tion network combined with recurrent layers in
DCNN architecture. We have empirically eval-
uated the recognition performance of the pro-
posed IRCNN model using different benchmark
datasets such as MNIST, CIFAR-10, CIFAR-
100, and SVHN. Experimental results show sim-
ilar or higher recognition accuracy when com-
pared to most of the popular DCNNs including
the RCNN. Furthermore, we have investigated
IRCNN performance against equivalent Incep-
tion Networks and Inception-Residual Networks
using the CIFAR-100 dataset. We report about
3.5%, 3.47% and 2.54% improvement in classiﬁ-
cation accuracy when compared to the RCNN,
equivalent Inception Networks, and Inception-
Residual Networks on the augmented CIFAR-
100 dataset respectively.
1. Introduction
In recent years, deep learning using Convolutional Neu-
ral Networks (CNNs) has shown enormous success in the
ﬁeld of machine learning and computer vision. CNNs pro-
vide state-of-the-art accuracy in various image recognition
tasks including object recognition (Schmidhuber, 2015;
Krizhevsky et al., 2012; Simonyan & Zisserman, 2014;
Szegedy et al., 2015), object detection (Girshick et al.,
2014), tracking (Wang et al., 2015), and image caption-
ing (Xu et al., 2014). In addition, this technique has been
applied massively in computer vision tasks such as video
representation and classiﬁcation of human activity (Bal-
las et al., 2015). Machine translation and natural language
processing are applied deep learning techniques that show
great success in this domain (Collobert & Weston, 2008;
Manning et al., 2014). Furthermore, this technique has
been used extensively in the ﬁeld of speech recognition
(Hinton et al., 2012). Moreover, deep learning is not lim-
ited to signal, natural language, image, and video process-
ing tasks, it has been applying successfully for game devel-
opment (Mnih et al., 2013; Lillicrap et al., 2015). There is
a lot of ongoing research for developing even better perfor-
mance and improving the training process of DCNNs (Lin
et al., 2013; Springenberg et al., 2014; Goodfellow et al.,
2013; Ioffe & Szegedy, 2015; Zeiler & Fergus, 2013).
In some cases, machine intelligence shows better perfor-
mance compared to human intelligence including calcula-
tion, chess, memory, and pattern matching. On the other
hand, human intelligence still provides better performance
in other ﬁelds such as object recognition, scene under-
standing, and more. Deep learning techniques (DCNNs
in particular) perform very well in the domains of detec-
tion, classiﬁcation, and scene understanding. There is a
still a gap that must be closed before human level intelli-
gence is reached when performing visual recognition tasks.
Machine intelligence may open an opportunity to build a
system that can process visual information the way that a
human brain does. According to the study on the visual
processing system within a human brain by James DiCarlo
et al. (Zoccolan & Rust, 2012) the brain consists of sev-
eral visual processing units starting with the visual cortex
arXiv:1704.07709v1 [cs.CV] 25 Apr 2017
2015-2017
Supervised
Im
age
Classification
Convolutional Neural Networks (CNNs)
By Debarko De @debarko
https://hackernoon.com/what-is-a-capsnet-or-capsule-network-2bfbe48769cc
SOCKEYE:
A Toolkit for Neural Machine Translation
Felix Hieber, Tobias Domhan, Michael Denkowski,
David Vilar, Artem Sokolov, Ann Clifton, Matt Post
{
fhieber
,
domhant
,
mdenkows
,
dvilar
,
artemsok
,
acclift
,
mattpost
}
@amazon.com
Abstract
We describe SOCKEYE,1 an open-source sequence-to-sequence toolkit for Neural
Machine Translation (NMT). SOCKEYE is a production-ready framework for
training and applying models as well as an experimental platform for researchers.
Written in Python and built on MXNET, the toolkit offers scalable training and
inference for the three most prominent encoder-decoder architectures: attentional
recurrent neural networks, self-attentional transformers, and fully convolutional
networks. SOCKEYE also supports a wide range of optimizers, normalization and
regularization techniques, and inference improvements from current NMT literature.
Users can easily run standard training recipes, explore different model settings, and
incorporate new ideas. In this paper, we highlight SOCKEYE’s features and bench-
mark it against other NMT toolkits on two language arcs from the 2017 Conference
on Machine Translation (WMT): English–German and Latvian–English. We report
competitive BLEU scores across all three architectures, including an overall best
score for SOCKEYE’s transformer implementation. To facilitate further comparison,
we release all system outputs and training scripts used in our experiments. The
SOCKEYE toolkit is free software released under the Apache 2.0 license.
1 Introduction
The past two years have seen a deep learning revolution bring rapid and dramatic change to the ﬁeld
of machine translation. For users, new neural network-based models consistently deliver better quality
translations than the previous generation of phrase-based systems. For researchers, Neural Machine
Translation (NMT) provides an exciting new landscape where training pipelines are simpliﬁed and
uniﬁed models can be trained directly from data. The promise of moving beyond the limitations of
Statistical Machine Translation (SMT) has energized the community, leading recent work to focus
almost exclusively on NMT and seemingly advance the state of the art every few months.
For all its success, NMT also presents a range of new challenges. While popular encoder-decoder
models are attractively simple, recent literature and the results of shared evaluation tasks show that
a signiﬁcant amount of engineering is required to achieve “production-ready” performance in both
translation quality and computational efﬁciency. In a trend that carries over from SMT, the strongest
NMT systems beneﬁt from subtle architecture modiﬁcations, hyper-parameter tuning, and empirically
effective heuristics. Unlike SMT, there is no “de-facto” toolkit that attracts most of the community’s
attention and thus contains all the best ideas from recent literature.2 Instead, the presence of many
independent toolkits3 brings diversity to the ﬁeld, but also makes it difﬁcult to compare architectural
and algorithmic improvements that are each implemented in different toolkits.
1
https://github.com/awslabs/sockeye
(version 1.12)
2For SMT, this role was largely ﬁlled by MOSES [Koehn et al., 2007].
3
https://github.com/jonsafari/nmt-list
arXiv:1712.05690v1 [cs.CL] 15 Dec 2017
Sequence to Sequence (seq2seq)
• seq2seq is a supervised learning algorithm where the
input is a sequence of tokens (for example, text,
audio) and the output generated is another
sequence of tokens.
• Example applications include:
• machine translation (input a sentence from
one language and predict what that sentence
would be in another language)
• text summarization (input a longer string of
words and predict a shorter string of words
that is a summary)
• speech-to-text (audio clips converted into
output sentences in tokens).
SOCKEYE:
A Toolkit for Neural Machine Translation
Felix Hieber, Tobias Domhan, Michael Denkowski,
David Vilar, Artem Sokolov, Ann Clifton, Matt Post
{
fhieber
,
domhant
,
mdenkows
,
dvilar
,
artemsok
,
acclift
,
mattpost
}
@amazon.com
Abstract
We describe SOCKEYE,1 an open-source sequence-to-sequence toolkit for Neural
Machine Translation (NMT). SOCKEYE is a production-ready framework for
training and applying models as well as an experimental platform for researchers.
Written in Python and built on MXNET, the toolkit offers scalable training and
inference for the three most prominent encoder-decoder architectures: attentional
recurrent neural networks, self-attentional transformers, and fully convolutional
networks. SOCKEYE also supports a wide range of optimizers, normalization and
regularization techniques, and inference improvements from current NMT literature.
Users can easily run standard training recipes, explore different model settings, and
incorporate new ideas. In this paper, we highlight SOCKEYE’s features and bench-
mark it against other NMT toolkits on two language arcs from the 2017 Conference
on Machine Translation (WMT): English–German and Latvian–English. We report
competitive BLEU scores across all three architectures, including an overall best
score for SOCKEYE’s transformer implementation. To facilitate further comparison,
we release all system outputs and training scripts used in our experiments. The
SOCKEYE toolkit is free software released under the Apache 2.0 license.
1 Introduction
The past two years have seen a deep learning revolution bring rapid and dramatic change to the ﬁeld
of machine translation. For users, new neural network-based models consistently deliver better quality
translations than the previous generation of phrase-based systems. For researchers, Neural Machine
Translation (NMT) provides an exciting new landscape where training pipelines are simpliﬁed and
uniﬁed models can be trained directly from data. The promise of moving beyond the limitations of
Statistical Machine Translation (SMT) has energized the community, leading recent work to focus
almost exclusively on NMT and seemingly advance the state of the art every few months.
For all its success, NMT also presents a range of new challenges. While popular encoder-decoder
models are attractively simple, recent literature and the results of shared evaluation tasks show that
a signiﬁcant amount of engineering is required to achieve “production-ready” performance in both
translation quality and computational efﬁciency. In a trend that carries over from SMT, the strongest
NMT systems beneﬁt from subtle architecture modiﬁcations, hyper-parameter tuning, and empirically
effective heuristics. Unlike SMT, there is no “de-facto” toolkit that attracts most of the community’s
attention and thus contains all the best ideas from recent literature.2 Instead, the presence of many
independent toolkits3 brings diversity to the ﬁeld, but also makes it difﬁcult to compare architectural
and algorithmic improvements that are each implemented in different toolkits.
1
https://github.com/awslabs/sockeye
(version 1.12)
2For SMT, this role was largely ﬁlled by MOSES [Koehn et al., 2007].
3
https://github.com/jonsafari/nmt-list
arXiv:1712.05690v1 [cs.CL] 15 Dec 2017
Sequence to Sequence (seq2seq)
• Recently, problems in this domain have been
successfully modeled with deep neural networks
that show a significant performance boost over
previous methodologies.
• Amazon released in open source the Sockeye
package, which uses Recurrent Neural Networks
(RNNs) and Convolutional Neural Network (CNN)
models with attention as encoder-decoder
architectures.
• https://github.com/awslabs/sockeye
2014-2017
Supervised
Text, Audio
Sequence to Sequence (seq2seq)
https://aws.amazon.com/blogs/machine-learning/train-neural-machine-translation-models-with-sockeye/
2014-2017
Supervised
Text, Audio
Sequence to Sequence (seq2seq)
https://aws.amazon.com/blogs/machine-learning/train-neural-machine-translation-models-with-sockeye/
“Das grüne Haus”
“the Green House”
2014-2017
Supervised
Text, Audio
K-Means Clustering
SOME METHODS FOR
CLASSIFICATION AND ANALYSIS
OF MULTIVARIATE OBSERVATIONS
J. MACQUEEN
UNIVERSITY OF CALIFORNIA, Los ANGELES
1. Introduction
The main purpose of this paper is to describe a process for partitioning an
N-dimensional population into k sets on the basis of a sample. The process,
which is called 'k-means,' appears to give partitions which are reasonably
efficient in the sense of within-class variance. That is, if p is the probability mass
function for the population, S = {S1, S2, -
* *, Sk} is a partition of EN, and ui,
i = 1, 2, * - , k, is the conditional mean of p over the set Si, then W2(S) =
ff=ISi
f z - u42 dp(z) tends to be low for the partitions S generated by the
method. We say 'tends to be low,' primarily because of intuitive considerations,
corroborated to some extent by mathematical analysis and practical computa-
tional experience. Also, the k-means procedure is easily programmed and is
computationally economical, so that it is feasible to process very large samples
on a digital computer. Possible applications include methods for similarity
grouping, nonlinear prediction, approximating multivariate distributions, and
nonparametric tests for independence among several variables.
In addition to suggesting practical classification methods, the study of k-means
has proved to be theoretically interesting. The k-means concept represents a
generalization of the ordinary sample mean, and one is naturally led to study the
pertinent asymptotic behavior, the object being to establish some sort of law of
large numbers for the k-means. This problem is sufficiently interesting, in fact,
for us to devote a good portion of this paper to it. The k-means are defined in
section 2.1, and the main results which have been obtained on the asymptotic
behavior are given there. The rest of section 2 is devoted to the proofs of these
results. Section 3 describes several specific possible applications, and reports
some preliminary results from computer experiments conducted to explore the
possibilities inherent in the k-means idea. The extension to general metric spaces
is indicated briefly in section 4.
The original point of departure for the work described here was a series of
problems in optimal classification (MacQueen [9]) which represented special
This work was supported by the Western Management Science Institute under a grant from
the Ford Foundation, and by the Office of Naval Research under Contract No. 233(75), Task
No. 047-041.
281
Bulletin de l’acad´
emie
polonaise des sciences
Cl. III — Vol. IV, No. 12, 1956
MATH´
EMATIQUE
Sur la division des corps mat´
eriels en parties 1
par
H. STEINHAUS
Pr´
esent´
e le 19 Octobre 1956
Un corps
Q
est, par d´
eﬁnition, une r´
epartition de mati`
ere dans l’espace,
donn´
ee par une fonction
f
(
P
) ; on appelle cette fonction la densit´
e du corps
en question ; elle est d´
eﬁnie pour tous les points
P
de l’espace ; elle est non-
n´
egative et mesurable. On suppose que l’ensemble caract´
eristique du corps
E
=E
P
{
f
(
P
)
>
0} est born´
e et de mesure positive ; on suppose aussi que
l’int´
egrale de
f
(
P
) sur
E
est ﬁnie : c’est la masse du corps
Q
. On consid`
ere
comme identiques deux corps dont les densit´
es sont ´
egales `
a un ensemble de
mesure nulle pr`
es.
En d´
ecomposant l’ensemble caract´
eristique d’un corps
Q
en
n
sous-ensembles
Ei
(
i
= 1
,
2
, . . . , n
) de mesures positives, on obtient une division du corps en
question en
n
corps partiels ; leurs ensembles caract´
eristiques respectifs sont
les
Ei
et leurs densit´
es sont d´
eﬁnies par les valeurs que prend la densit´
e du
corps
Q
dans ces ensembles partiels. En d´
esignant les corps partiels par
Qi
, on
´
ecrira
Q
=
Q1
+
Q2
+
. . .
+
Qn
. Quand on donne d’abord
n
corps
Qi
, dont les
ensembles caract´
eristiques sont disjoints deux `
a deux `
a la mesure nulle pr`
es, il
existe ´
evidemment un corps
Q
ayant ces
Qi
comme autant de parties ; on ´
ecrira
Q1
+
Q2
+
. . .
+
Qn
=
Q
. Ces remarques su sent pour expliquer la division et
la composition des corps.
Le
probl`
eme
de cette Note est la division d’un corps en
n
parties
Ki
(
i
= 1
,
2
, . . . , n
) et le choix de
n
points
Ai
de mani`
ere `
a rendre aussi petite que
possible la somme
(1)
S
(
K, A
) =
n
X
i=1
I
(
Ki, Ai
) (
K
⌘ {
Ki
}
, A
⌘ {
Ai
})
,
o`
u
I
(
Q, P
) d´
esigne, en g´
en´
eral, le moment d’inertie d’un corps quelconque
Q
par rapport `
a un point quelconque
P
. Pour traiter ce probl`
eme ´
el´
ementaire nous
aurons recours aux lemmes suivants :
1. Cet article de Hugo Steinhaus est le premier formulant de mani`
ere explicite, en dimen-
sion ﬁnie, le probl`
eme de partitionnement par les k-moyennes (k-means), dites aussi “nu´
ees
dynamiques”. Son algorithme classique est le mˆ
eme que celui de la quantiﬁcation optimale de
Lloyd-Max. ´
Etant di cilement accessible sous format num´
erique, le voici transduit par Maciej
Denkowski, transmis par J´
erˆ
ome Bolte, transcrit par Laurent Duval, en juillet/aoˆ
ut 2015. Un
e↵ort a ´
et´
e fourni pour conserver une proximit´
e avec la pagination originale.
801
1956-1967
U
nsupervised
Clustering
K-Means Clustering
1956-1967
U
nsupervised
Clustering
Clustering converges
when the centers
“don’t move” anymore
Principal Component Analysis (PCA)
• PCA is an unsupervised learning algorithm that
attempts to reduce the dimensionality (number
of features) within a dataset while still retaining
as much information as possible
• This is done by finding a new set of features
called components, which are composites of the
original features that are uncorrelated with one
another
• They are also constrained so that the first
component accounts for the largest possible
variability in the data, the second component the
second most variability, and so on
Pearson, K. 1901. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2:559-572.
http://pbil.univ-lyon1.fr/R/pearson1901.pdf
1901
U
nsupervised
D
im
ensionality
Reduction
Principal Component Analysis (PCA)
1901
U
nsupervised
D
im
ensionality
Reduction
Principal Component Analysis (PCA)
1901
U
nsupervised
D
im
ensionality
Reduction
Latent Dirichlet Allocation (LDA)
Copyright 2000 by the Genetics Society of America
Inference of Population Structure Using Multilocus Genotype Data
Jonathan K. Pritchard, Matthew Stephens and Peter Donnelly
Department of Statistics, University of Oxford, Oxford OX1 3TG, United Kingdom
Manuscript received September 23, 1999
Accepted for publication February 18, 2000
ABSTRACT
We describe a model-based clustering method for using multilocus genotype data to infer population
structure and assign individuals to populations. We assume a model in which there are K populations
(where K may be unknown), each of which is characterized by a set of allele frequencies at each locus.
Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more popula-
tions if their genotypes indicate that they are admixed. Our model does not assume a particular mutation
process, and it can be applied to most of the commonly used genetic markers, provided that they are not
closely linked. Applications of our method include demonstrating the presence of population structure,
assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individu-
als. We showthat the method can produce highlyaccurate assignments using modest numbers of loci—e.g.,
seven microsatellite loci in an example using genotype data from an endangered bird species. The software
used for this article is available from http:// www.stats.ox.ac.uk/ zpritch/ home.html.
IN applications of population genetics, it is often use- populationsbased on these subjective criteria represents
a natural assignment in genetic terms, and it would be
ful to classify individuals in a sample into popula-
tions. In one scenario, the investigator begins with a useful to be able to conﬁrm that subjective classiﬁcations
are consistent with genetic information and hence ap-
sample of individuals and wants to say something about
the properties of populations. For example, in studies propriate for studying the questions of interest. Further,
there are situations where one is interested in “cryptic”
of human evolution, the population is often considered
to be the unit of interest, and a great deal of work has population structure—i.e., population structure that is
difﬁcult to detect using visible characters, but may be
focused on learning about the evolutionary relation-
ships of modern populations (e.g., Caval l i et al. 1994). signiﬁcant in genetic terms. For example, when associa-
tion mapping is used to ﬁnd disease genes, the presence
In a second scenario, the investigator begins with a set
of predeﬁned populations and wishes to classifyindivid- of undetected population structure can lead to spurious
associations and thus invalidate standard tests (Ewens
uals of unknown origin. This type of problem arises
in many contexts (reviewed by Davies et al. 1999). A and Spiel man 1995). The problem of cryptic population
structure also arises in the context of DNA ﬁngerprint-
standard approach involves sampling DNA from mem-
bers of a number of potential source populations and ing for forensics, where it is important to assess the
degree of population structure to estimate the probabil-
using these samples to estimate allele frequencies in
ity of false matches (Bal ding and Nich ol s 1994, 1995;
each population at a series of unlinked loci. Using the
For eman et al. 1997; Roeder et al. 1998).
estimated allele frequencies, it is then possible to com-
Pr it ch ar d and Rosenber g (1999) considered how
pute the likelihood that a given genotype originated in
genetic information might be used to detect the pres-
each population. Individuals of unknown origin can be
ence of cryptic population structure in the association
assigned to populations according to these likelihoods
mapping context. More generally, one would like to be
Paet kau et al. 1995; Rannal a and Mount ain 1997).
able to identify the actual subpopulations and assign
In both situations described above, a crucial ﬁrst step
individuals (probabilistically) to these populations. In
is to deﬁne a set of populations. The deﬁnition of popu-
this article we use a Bayesian clustering approach to
lations is typically subjective, based, for example, on
tackle this problem. We assume a model in which there
linguistic, cultural, or physical characters, as well as the
are K populations (where K may be unknown), each of
geographic location of sampled individuals. This subjec-
which is characterized by a set of allele frequencies at
tive approach is usually a sensible way of incorporating
each locus. Our method attempts to assign individuals
diverse types of information. However, it maybe difﬁcult
to populations on the basis of their genotypes, while
to know whether a given assignment of individuals to
simultaneously estimating population allele frequen-
cies. The method can be applied to various types of
markers [e.g., microsatellites, restriction fragment
Corresponding author: Jonathan Pritchard, Department of Statistics,
length polymorphisms (RFLPs), or single nucleotide
University of Oxford, 1 S. Parks Rd., Oxford OX1 3TG, United King-
dom. E-mail: pritch@stats.ox.ac.uk polymorphisms (SNPs)], but it assumes that the marker
Genetics 155: 945–959 ( June 2000)
Journal of Machine Learning Research 3 (2003) 993-1022 Submitted 2/02; Published 1/03
Latent Dirichlet Allocation
David M. Blei [email protected]
Computer Science Division
University of California
Berkeley, CA 94720, USA
Andrew Y. Ng [email protected]
Computer Science Department
Stanford University
Stanford, CA 94305, USA
Michael I. Jordan [email protected]
Computer Science Division and Department of Statistics
University of California
Berkeley, CA 94720, USA
Editor: John Lafferty
Abstract
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of
discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each
item of a collection is modeled as a ﬁnite mixture over an underlying set of topics. Each topic is, in
turn, modeled as an inﬁnite mixture over an underlying set of topic probabilities. In the context of
text modeling, the topic probabilities provide an explicit representation of a document. We present
efﬁcient approximate inference techniques based on variational methods and an EM algorithm for
empirical Bayes parameter estimation. We report results in document modeling, text classiﬁcation,
and collaborative ﬁltering, comparing to a mixture of unigrams model and the probabilistic LSI
model.
1. Introduction
In this paper we consider the problem of modeling text corpora and other collections of discrete
data. The goal is to ﬁnd short descriptions of the members of a collection that enable efﬁcient
processing of large collections while preserving the essential statistical relationships that are useful
for basic tasks such as classiﬁcation, novelty detection, summarization, and similarity and relevance
judgments.
Signiﬁcant progress has been made on this problem by researchers in the ﬁeld of informa-
tion retrieval (IR) (Baeza-Yates and Ribeiro-Neto, 1999). The basic methodology proposed by
IR researchers for text corpora—a methodology successfully deployed in modern Internet search
engines—reduces each document in the corpus to a vector of real numbers, each of which repre-
sents ratios of counts. In the popular tf-idf scheme (Salton and McGill, 1983), a basic vocabulary
of “words” or “terms” is chosen, and, for each document in the corpus, a count is formed of the
number of occurrences of each word. After suitable normalization, this term frequency count is
compared to an inverse document frequency count, which measures the number of occurrences of a
c 2003 David M. Blei, Andrew Y. Ng and Michael I. Jordan.
2000-2003
U
nsupervised
Topic
M
odeling
Latent Dirichlet Allocation (LDA)
• As an extremely simple example, given a set of documents where the
only words that occur within them are eat, sleep, play, meow, and
bark, LDA might produce topics like the following:
Topic eat sleep play meow bark
Cats? Topic 1 0.1 0.3 0.2 0.4 0.0
Dogs? Topic 2 0.2 0.1 0.4 0.0 0.3
2000-2003
U
nsupervised
Topic
M
odeling
Neural Topic Model (NTM)
Encoder: feedforward net
Input term counts vector
µ
z
Document
Posterior
Sampled Document
Representation
Decoder:
Softmax
Output term counts vector
A Novel Neural Topic Model and Its Supervised Extension
Ziqiang Cao1 Sujian Li1 Yang Liu1 Wenjie Li2 Heng Ji3
1Key Laboratory of Computational Linguistics, Peking University, MOE, China
2Computing Department, Hong Kong Polytechnic University, Hong Kong
3Computer Science Department, Rensselaer Polytechnic Institute, USA
{ziqiangyeah, lisujian, pku7yang}@pku.edu.cn [email protected] [email protected]
Abstract
Topic modeling techniques have the beneﬁts of model-
ing words and documents uniformly under a probabilis-
tic framework. However, they also suffer from the limi-
tations of sensitivity to initialization and unigram topic
distribution, which can be remedied by deep learning
techniques. To explore the combination of topic mod-
eling and deep learning techniques, we ﬁrst explain the
standard topic model from the perspective of a neural
network. Based on this, we propose a novel neural topic
model (NTM) where the representation of words and
documents are efﬁciently and naturally combined into a
uniform framework. Extending from NTM, we can eas-
ily add a label layer and propose the supervised neu-
ral topic model (sNTM) to tackle supervised tasks. Ex-
periments show that our models are competitive in both
topic discovery and classiﬁcation/regression tasks.
Introduction
The real-world tasks of text categorization and document
retrieval rely critically on a good representation of words
and documents. So far, state-of-the-art techniques including
topic models (Blei, Ng, and Jordan 2003; Mcauliffe and Blei
2007; Wang, Blei, and Li 2009; Ramage et al. 2009) and
neural networks (Bengio et al. 2003; Hinton and Salakhutdi-
nov 2009; Larochelle and Lauly 2012) have shown remark-
able success in exploring semantic representations of words
and documents. Such models are usually embedded with la-
tent variables or topics, which serve the role of capturing the
efﬁcient low-dimensional representation of words and doc-
uments.
Topic modeling techniques, such as Latent Dirichlet Allo-
cation (LDA) (Blei, Ng, and Jordan 2003), have been widely
used for inferring a low dimensional representation that cap-
tures the latent semantics of words and documents. Each
topic is deﬁned as a distribution over words and each docu-
ment as a mixture distribution over topics. Thus, the seman-
tic representations of both words and documents are com-
bined into a uniﬁed framework which has a strict proba-
bilistic explanation. However, topic models also suffer from
certain limitations as follows. First, LDA-based models re-
quire prior distributions which are always difﬁcult to deﬁne.
Copyright c 2015, Association for the Advancement of Artiﬁcial
Intelligence (www.aaai.org). All rights reserved.
Second, previous models rarely adopt
n
-grams beyond uni-
grams in document modeling due to the sparseness problem,
though
n
-grams are important to express text. Last, when
there is extra labeling information associated with docu-
ments, topic models have to do some task-speciﬁc transfor-
mation in order to make use of it (Mcauliffe and Blei 2007;
Wang, Blei, and Li 2009; Ramage et al. 2009), which may
be computationally costly.
Recently, deep learning techniques also make low di-
mensional representations (i.e., distributed representations)
of words (i.e., word embeddings) and documents (Bengio
et al. 2003; Mnih and Hinton 2007; Collobert and Weston
2008; Mikolov et al. 2013; Ranzato and Szummer 2008;
Hinton and Salakhutdinov 2009; Larochelle and Lauly 2012;
Srivastava, Salakhutdinov, and Hinton 2013) feasible. Word
embeddings provide a way of representing phrases (Mikolov
et al. 2013) and are easy to embed with supervised tasks
(Collobert et al. 2011). With layer-wise pre-training (Ben-
gio et al. 2007), neural networks are built to automatically
initialize their weight values. Yet, the main problem of deep
learning is that it is hard to give each dimension of the gener-
ated distributed representations a reasonable interpretation.
Based on the analysis above, we can see that current topic
modeling and deep learning techniques both exhibit their
strengths and defects in representing words and documents.
A question comes to our mind: Can these two kinds of tech-
niques be combined to represent words and documents si-
multaneously? This combination can on the one hand over-
come the computation complexity of topic models and on
the other hand provide a reasonable probabilistic explana-
tion of the hidden variables.
In our preliminary study we explain topic models from
the perspective of a neural network, starting from the fact
that the conditional probability of a word given a document
can be seen as the product of the probability of a word
given a topic (word-topic representation) and the probabil-
ity of a topic given the document (topic-document represen-
tation). At the same time, to solve the unigram topic dis-
tribution problem of a standard topic model, we make use
of the word embeddings available (Mikolov et al. 2013) to
represent
n
-grams. Based on the neural network explanation
and
n
-gram representation, we propose a novel neural topic
model (NTM) where two hidden layers are constructed to
efﬁciently acquire the
n
-gram topic and topic-document rep-
2015
U
nsupervised
Topic
M
odeling
Time Series Forecasting (DeepAR)
DeepAR: Probabilistic Forecasting with
Autoregressive Recurrent Networks
Valentin Flunkert ⇤
, David Salinas ⇤
, Jan Gasthaus
Amazon Development Center
Germany
Abstract
Probabilistic forecasting, i.e. estimating the probability distribution of a time se-
ries’ future given its past, is a key enabler for optimizing business processes. In
retail businesses, for example, forecasting demand is crucial for having the right
inventory available at the right time at the right place. In this paper we propose
DeepAR, a methodology for producing accurate probabilistic forecasts, based on
training an auto-regressive recurrent network model on a large number of related
time series. We demonstrate how by applying deep learning techniques to fore-
casting, one can overcome many of the challenges faced by widely-used classical
approaches to the problem. We show through extensive empirical evaluation on
several real-world forecasting data sets that our methodology produces more accu-
rate forecasts than other state-of-the-art methods, while requiring minimal manual
work.
1 Introduction
Forecasting plays a key role in automating and optimizing operational processes in most businesses
and enables data driven decision making. In retail for example, probabilistic forecasts of product
supply and demand can be used for optimal inventory management, staff scheduling and topology
planning [17], and are more generally a crucial technology for most aspects of supply chain opti-
mization.
The prevalent forecasting methods in use today have been developed in the setting of forecasting
individual or small groups of time series. In this approach, model parameters for each given time
series are independently estimated from past observations. The model is typically manually selected
to account for different factors, such as autocorrelation structure, trend, seasonality, and other ex-
planatory variables. The ﬁtted model is then used to forecast the time series into the future according
to the model dynamics, possibly admitting probabilistic forecasts through simulation or closed-form
expressions for the predictive distributions. Many methods in this class are based on the classical
Box-Jenkins methodology [3], exponential smoothing techniques, or state space models [11, 18].
In recent years, a new type of forecasting problem has become increasingly important in many appli-
cations. Instead of needing to predict individual or a small number of time series, one is faced with
forecasting thousands or millions of related time series. Examples include forecasting the energy
consumption of individual households, forecasting the load for servers in a data center, or forecast-
ing the demand for all products that a large retailer offers. In all these scenarios, a substantial amount
of data on past behavior of similar, related time series can be leveraged for making a forecast for an
individual time series. Using data from related time series not only allows ﬁtting more complex (and
hence potentially more accurate) models without overﬁtting, it can also alleviate the time and labor
intensive manual feature engineering and model selection steps required by classical techniques.
⇤equal contribution
arXiv:1704.04110v2 [cs.AI] 5 Jul 2017
2017
Supervised
Tim
e
Series Forecasting
• DeepAR is a supervised learning algorithm for
forecasting scalar time series using recurrent neural
networks (RNN)
• Classical forecasting methods fit one model to each
individual time series, and then use that model to
extrapolate the time series into the future
• In many applications you might have many similar time
series across a set of cross-sectional units
• For example, demand for different products, load of servers,
requests for web pages, and so on
• In this case, it can be beneficial to train a single model
jointly over all of these time series
• DeepAR takes this approach, training a model for predicting a
time series over a large set of (related) time series
Time Series Forecasting (DeepAR)
2017
Supervised
Tim
e
Series Forecasting
BlazingText (Word2vec)
BlazingText: Scaling and Accelerating Word2Vec using Multiple
GPUs
Saurabh Gupta
Amazon Web Services
[email protected]
Vineet Khare
Amazon Web Services
[email protected]
ABSTRACT
Word2Vec is a popular algorithm used for generating dense vector
representations of words in large corpora using unsupervised learn-
ing. The resulting vectors have been shown to capture semantic
relationships between the corresponding words and are used ex-
tensively for many downstream natural language processing (NLP)
tasks like sentiment analysis, named entity recognition and machine
translation. Most open-source implementations of the algorithm
have been parallelized for multi-core CPU architectures including
the original C implementation by Mikolov et al. [1] and FastText
[2] by Facebook. A few other implementations have attempted to
leverage GPU parallelization but at the cost of accuracy and scal-
ability. In this work, we present BlazingText, a highly optimized
implementation of word2vec in CUDA, that can leverage multiple
GPUs for training. BlazingText can achieve a training speed of up to
43M words/sec on 8 GPUs, which is a 9x speedup over 8-threaded
CPU implementations, with minimal eect on the quality of the
embeddings.
CCS CONCEPTS
• Computing methodologies → Neural networks; Natural
language processing;
KEYWORDS
Word embeddings, Word2Vec, Natural Language Processing, Ma-
chine Learning, CUDA, GPU
ACM Reference format:
Saurabh Gupta and Vineet Khare. 2017. BlazingText: Scaling and Accelerat-
ing Word2Vec using Multiple GPUs. In Proceedings of MLHPC’17: Machine
Learning in HPC Environments, Denver, CO, USA, November 12–17, 2017,
5 pages.
https://doi.org/10.1145/3146347.3146354
1 INTRODUCTION
Word2Vec aims to represent each word as a vector in a low-dimensional
embedding space such that the geometry of resulting vectors cap-
tures word semantic similarity through the cosine similarity of cor-
responding vectors as well as more complex relationships through
vector subtractions, such as vec(“King”) - vec(“Queen”) + vec(“Woman”)
MLHPC’17: Machine Learning in HPC Environments, November 12–17, 2017, Denver, CO,
USA
© 2017 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-5137-9/17/11.
https://doi.org/10.1145/3146347.3146354
⇡ vec(“Man”). This idea has enabled many Natural Language Pro-
cessing (NLP) algorithms to achieve better performance [3, 4].
The optimization in word2vec is done using Stochastic Gradient
Descent (SGD), which solves the problem iteratively; at each step,
it picks a pair of words: an input word and a target word either
from its window or a random negative sample. It then computes the
gradients of the objective function with respect to the two chosen
words, and updates the word representations of the two words
based on the gradient values. The algorithm then proceeds to the
next iteration with a dierent word pair being chosen.
One of the main issues with SGD is that it is inherently sequential;
since there is a dependency between the update from one iteration
and the computation in the next iteration (they may happen to touch
the same word representations), each iteration must potentially wait
for the update from the previous iteration to complete. This does
not allow us to use the parallel resources of the hardware.
However, to solve the above issue, word2vec uses Hogwild [5],
a scheme where dierent threads process dierent word pairs in
parallel and ignore any conicts that may arise in the model up-
date phases. In theory, this can reduce the rate of convergence of
algorithm as compared to a sequential run. However, the Hogwild
approach has been shown to work well in the case updates across
threads are unlikely to be to the same word; and indeed for large
vocabulary sizes, conicts are relatively rare and convergence is
not typically aected.
The success of Hogwild approach for Word2Vec in case of multi-
core architectures makes this algorithm a good candidate for ex-
ploiting GPU, which provides orders of magnitude more parallelism
than a CPU. In this paper, we propose an ecient parallelization
technique for accelerating word2vec using GPUs.
GPU acceleration using deep learning frameworks is not a good
choice for accelerating word2vec [6]. These frameworks are often
suitable for “deep networks” where the computation is dominated
by heavy operations like convolutions and large matrix multiplica-
tions. On the other hand, word2vec is a relatively shallow network,
as each training step consists of an embedding lookup, gradient
computation and nally weight updates for the word pair under
consideration. The gradient computation and updates involve small
dot products and thus don’t benet from the use of cuDNN [7] or
cuBLAS [8] libraries.
The limitations of deep learning frameworks led us to explore
the CUDA C++ API. We design the training algorithm from scratch,
to utilize CUDA multi-threading capabilities optimally, without
hurting the output accuracy by over-exploiting GPU parallelism.
Finally, to scale out BlazingText to process text corpus at several
million words/sec, we demonstrate the possibility of using multiple
GPUs to perform data parallelism based training, which is one of the
main contributions of our work. We benchmark BlazingText against
2013-2017
Supervised
W
ord
Em
bedding
Efﬁcient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
[email protected]
Kai Chen
Google Inc., Mountain View, CA
[email protected]
Greg Corrado
Google Inc., Mountain View, CA
[email protected]
Jeffrey Dean
Google Inc., Mountain View, CA
[email protected]
Abstract
We propose two novel model architectures for computing continuous vector repre-
sentations of words from very large data sets. The quality of these representations
is measured in a word similarity task, and the results are compared to the previ-
ously best performing techniques based on different types of neural networks. We
observe large improvements in accuracy at much lower computational cost, i.e. it
takes less than a day to learn high quality word vectors from a 1.6 billion words
data set. Furthermore, we show that these vectors provide state-of-the-art perfor-
mance on our test set for measuring syntactic and semantic word similarities.
1 Introduction
Many current NLP systems and techniques treat words as atomic units - there is no notion of similar-
ity between words, as these are represented as indices in a vocabulary. This choice has several good
reasons - simplicity, robustness and the observation that simple models trained on huge amounts of
data outperform complex systems trained on less data. An example is the popular N-gram model
used for statistical language modeling - today, it is possible to train N-grams on virtually all available
data (trillions of words [3]).
However, the simple techniques are at their limits in many tasks. For example, the amount of
relevant in-domain data for automatic speech recognition is limited - the performance is usually
dominated by the size of high quality transcribed speech data (often just millions of words). In
machine translation, the existing corpora for many languages contain only a few billions of words
or less. Thus, there are situations where simple scaling up of the basic techniques will not result in
any signiﬁcant progress, and we have to focus on more advanced techniques.
With progress of machine learning techniques in recent years, it has become possible to train more
complex models on much larger data set, and they typically outperform the simple models. Probably
the most successful concept is to use distributed representations of words [10]. For example, neural
network based language models signiﬁcantly outperform N-gram models [1, 27, 17].
1.1 Goals of the Paper
The main goal of this paper is to introduce techniques that can be used for learning high-quality word
vectors from huge data sets with billions of words, and with millions of words in the vocabulary. As
far as we know, none of the previously proposed architectures has been successfully trained on more
1
arXiv:1301.3781v3 [cs.CL] 7 Sep 2013
Word2vec ⇾ Word Embedding
2013
Supervised
W
ord
Em
bedding
Contextual
Bag-Of-Words
(CBOW)
to predict a word
given its context
Skip-Gram with
Negative Sampling
(SGNS)
to predict the context
given a word
@data_monsters
https://twitter.com/data_monsters/status/844256398393462784
BlazingText (Word2vec) Scaling
2017
Supervised
W
ord
Em
bedding
Our Customers use ML at a massive scale
“We collect 160M events
daily in the ML pipeline
and run training over the
last 15 days and need it to
complete in one hour.
Effectively there's 100M
features in the model.”
Valentino Volonghi, CTO
“We process 3 million ad
requests a second,
100,000 features per
request. That’s 250 trillion
per day. Not your run of
the mill Data science
problem!”
Bill Simmons, CTO
“Our data warehouse is
100TB and we are
processing 2TB daily.
We're running mostly
gradient boosting (trees),
LDA and K-Means
clustering and collaborative
filtering.“
Shahar Cizer Kobrinsky,
VP Architecture
Machine Learning
Large Scale Machine Learning
Model Selection (Hyperparameters)
1
1
Incremental Training
2
3
1
2
What about Streaming?
State
Streaming ⇾ Infinitely Scalable
Data Size
Memory
Data Size
Time/Cost
Incremental Training with Streaming
2
3
1
2
Incremental Training with Streaming
3
1
2
Supporting GPU/CPU
GPU State
Distributed
GPU State
GPU State
GPU State
Shared State
GPU
GPU
GPU Local
State
Shared
State
Local
State
Local
State
State ≥ Model
GPU State
what is the effect
of different
hyperparameters?
Model Selection with Streaming
1
1
Model Selection with Streaming
1
trying different
hyperparameters
Abstraction and Containerization
def
initialize(...)
def update(...)
def finalize(...)
Amazon SageMaker
• Hosted Jupyter notebooks that
require no setup, so that you can
start processing your training
dataset and developing your
algorithms immediately
• One-click, on-demand distributed
training that sets up and tears
down the cluster after training.
• Built-in, high-performance ML
algorithms, re-engineered for
greater, speed, accuracy, and
data-throughput
Exploration Training
Hosting
Amazon SageMaker
• Built-in model tuning
(hyperparameter optimization)
that can automatically adjust
hundreds of different
combinations of algorithm
parameters
• An elastic, secure, and scalable
environment to host your models,
with one-click deployment
Amazon SageMaker
https://github.com/aws/sagemaker-tensorflow-containers
https://github.com/aws/sagemaker-mxnet-containers
https://github.com/aws/sagemaker-container-support
https://github.com/aws/sagemaker-spark
https://github.com/aws/sagemaker-python-sdk
O
pen
Source
AW
S
Sum
m
it
M
ilan
2018
Machine Learning Inference at the Edge
AWS
Services
Used
Amazon
SageMaker
AWS
Greengrass
Mobile
Edge
Cloud
4G
Network
Vodafone
Capabilities
Used
Services
Solution & The Benefits AW
S
Sum
m
it
M
ilan
2018
Use of AWS Greengrass • Seamlessly extends AWS cloud
capabilities to devices
• Integrates edge computing
with cloud natively
• Speed: Proof of Concept realised
in 7 weeks
• Future-ready: Can enrich application features +
apply concept
to other use cases
• A showcase: For future applications
Off-load of compute
from camera to Telco
Edge Cloud
• Lower Bill of Material
• Decoupling the life cycles:
Car versus Cloud
• Real-time performance
Machine Learning = Algorithms + Data + Tools
And Then There Are Algorithms
Algorithm Scope
Infinitely
Scalable
Linear Learner classification, regression Y
Factorization Machines classification, regression, sparse datasets Y
XGBoost regression, classification (binary and multiclass), and ranking
Image Classification CNNs (ResNet, DenseNet, Inception)
Sequence to Sequence (seq2seq) translation, text summarization, speech-to-text (RNNs, CNN)
K-Means Clustering clustering, unsupervised Y
Principal Component Analysis (PCA) dimensionality reduction, unsupervised Y
Latent Dirichlet Allocation (LDA) topic modeling, unsupervised
Neural Topic Model (NTM) topic modeling, unsupervised Y
Time Series Forecasting (DeepAR) time series forecasting (RNN) Y
BlazingText (Word2vec) word embeddings
And Then There Are Algorithms
Danilo Poccia
Evangelist, Serverless
[email protected]
@danilop
danilop