Lock in $30 Savings on PRO—Offer Ends Soon! ⏳
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
文献紹介 / Knowledge Tracing with GNN
Search
Atom
December 04, 2020
0
76
文献紹介 / Knowledge Tracing with GNN
文献紹介と書いてあるが自分の用のメモ
公開しなくても良いかなと思ったが公開
Atom
December 04, 2020
Tweet
Share
More Decks by Atom
See All by Atom
文献紹介 / Structure-based Knowledge Tracing: An Influence Propagation View
roraidolaurent
0
76
文献紹介 / Non-Intrusive Parametric Reduced Order Models withHigh-Dimensional Inputs via Gradient-Free Active Subspace
roraidolaurent
0
45
ニューラルネットワークのベイズ推論 / Bayesian inference of neural networks
roraidolaurent
1
2.7k
Graph Convolutional Networks
roraidolaurent
0
200
文献紹介 / A Probabilistic Annotation Model for Crowdsourcing Coreference
roraidolaurent
0
54
文献紹介Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
roraidolaurent
0
84
文献紹介/ Bayesian Learning for Neural Dependency Parsing
roraidolaurent
0
88
ポッキー数列の加法定理 / Pocky number additon theorem
roraidolaurent
0
190
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
roraidolaurent
1
120
Featured
See All Featured
No one is an island. Learnings from fostering a developers community.
thoeni
19
3k
Art, The Web, and Tiny UX
lynnandtonic
297
20k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
229
52k
How To Stay Up To Date on Web Technology
chriscoyier
789
250k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
6
470
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
93
17k
Statistics for Hackers
jakevdp
796
220k
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
47
2.1k
How to train your dragon (web standard)
notwaldorf
88
5.7k
We Have a Design System, Now What?
morganepeng
50
7.2k
Bash Introduction
62gerente
608
210k
Large-scale JavaScript Application Architecture
addyosmani
510
110k
Transcript
None
None
∈ , ∈ {0,1}2 ∈ {0,1} ∈ {0,1} + 1
≡ +1 = , , , = 1 , ⋯ , ⊆ × , ∈ ℝ× ∈ ∈ ℝ
None
∈ ℝ2× ∈ ℝ× () ∈ ℝ ∈
, ℎ
None
None
None
None
None
None
☓
None
None
∈ {0,1}
None
None
None
−1 から問題 (スキル をもつ)に正答確率を アテンションで求めるが, と同じスキルをもつ問題(例 )
の正誤情報 は −1 では失われている可能性が大きい. に関連する問題を選択し, その情報 についても アテンションをとる.
None
None
None
None
None
None
None