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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
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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
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
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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
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