Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E ff i cient Sparsity 10.A Survey on Vision Transformer
Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E ff i cient Sparsity 10.A Survey on Vision Transformer ษڧձͰѻͬͨจ
Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E ff i cient Sparsity 10.A Survey on Vision Transformer ᶃTransformerͷը૾ͷԠ༻ͷReview
Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E ff i cient Sparsity 10.A Survey on Vision Transformer ᶄBNͳ͠ͰEf fi cientNet͑ͨ࠷ઌϞσϧ
Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E fi cientNet͑ͨ࠷ઌϞσϧ E ffi cientNetV2:
Smaller Models and Faster Training ᶄ͞ΒʹͦΕΛ͑ͨEf fi cientNetV2
Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E ff i cient Sparsity 10.A Survey on Vision Transformer ᶅجຊ: ॳ৺ऀ/ݚڀऀ͚
Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E ff i cient Sparsity 10.A Survey on Vision Transformer ɾώϯτϯڭतͷϞσϧͷσβΠϯఏএͷPaper
Top Recent in Last Year 1. Transformers in Vision: A Survey 2. The Modern Mathematics of Deep Learning 3. High-Performance Large-Scale Image Recognition Without Normalization 4. Cross-validation: what does it estimate and how well does it do it? 5. How to avoid machine learning pitfalls: a guide for academic researchers 6. How to represent part-whole hierarchies in a neural network 7. Point Transformer 8. Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and E ff i cient Sparsity 10.A Survey on Vision Transformer ɾޯ߱Լ๏Ͱͷಛநग़Χʔωϧ๏ͱࣅ͍ͯΔ
ailaboocu
matleenalaakso
kfujita
cm_manaus
shiba_8ro
akane69
rlifchitz
signer
faarsheik
tam07pb915
knocklearn
cbtlibrary
aarron
3n
addyosmani
sachag
bluesmoon
paulrobertlloyd
chrislema
sonatard
hatefulcrawdad
trallard
trishagee
eileencodes