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Category:Research
Pages in category "Research"
The following 134 pages are in this category, out of 134 total.
A
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks/en
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks/es
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks/paper
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks/paper/en
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks/paper/es
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks/paper/zh
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks/zh
- Adam A Method for Stochastic Optimization
- Adam A Method for Stochastic Optimization/en
- Adam A Method for Stochastic Optimization/es
- Adam A Method for Stochastic Optimization/zh
- Attention Is All You Need
- Attention Is All You Need/en
- Attention Is All You Need/es
- Attention Is All You Need/zh
B
- Batch Normalization Accelerating Deep Network Training
- Batch Normalization Accelerating Deep Network Training/en
- Batch Normalization Accelerating Deep Network Training/es
- Batch Normalization Accelerating Deep Network Training/zh
- BERT Pre-training of Deep Bidirectional Transformers
- BERT Pre-training of Deep Bidirectional Transformers/en
- BERT Pre-training of Deep Bidirectional Transformers/es
- BERT Pre-training of Deep Bidirectional Transformers/zh
D
- DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
- DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems/en
- DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems/es
- DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems/paper
- DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems/paper/en
- DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems/zh
- Decoupled Weight Decay Regularization
- Decoupled Weight Decay Regularization/en
- Decoupled Weight Decay Regularization/es
- Decoupled Weight Decay Regularization/paper
- Decoupled Weight Decay Regularization/paper/en
- Decoupled Weight Decay Regularization/paper/es
- Decoupled Weight Decay Regularization/paper/zh
- Decoupled Weight Decay Regularization/zh
- Deep & Cross Network for Ad Click Predictions
- Deep & Cross Network for Ad Click Predictions/en
- Deep & Cross Network for Ad Click Predictions/es
- Deep & Cross Network for Ad Click Predictions/paper
- Deep & Cross Network for Ad Click Predictions/paper/en
- Deep & Cross Network for Ad Click Predictions/paper/es
- Deep & Cross Network for Ad Click Predictions/paper/zh
- Deep & Cross Network for Ad Click Predictions/zh
- Deep Residual Learning for Image Recognition
- Deep Residual Learning for Image Recognition/en
- Deep Residual Learning for Image Recognition/es
- Deep Residual Learning for Image Recognition/zh
- Dropout A Simple Way to Prevent Overfitting
- Dropout A Simple Way to Prevent Overfitting/en
- Dropout A Simple Way to Prevent Overfitting/es
- Dropout A Simple Way to Prevent Overfitting/zh
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting/en
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting/es
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting/paper
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting/paper/en
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting/paper/es
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting/paper/zh
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting/zh
E
G
I
- ImageNet Classification with Deep CNNs
- ImageNet Classification with Deep CNNs/en
- ImageNet Classification with Deep CNNs/es
- ImageNet Classification with Deep CNNs/zh
- Incorporating Nesterov Momentum into Adam
- Incorporating Nesterov Momentum into Adam/en
- Incorporating Nesterov Momentum into Adam/es
- Incorporating Nesterov Momentum into Adam/paper
- Incorporating Nesterov Momentum into Adam/paper/en
- Incorporating Nesterov Momentum into Adam/paper/es
- Incorporating Nesterov Momentum into Adam/paper/zh
- Incorporating Nesterov Momentum into Adam/zh
L
- Language Modeling with Gated Convolutional Networks
- Language Modeling with Gated Convolutional Networks/en
- Language Modeling with Gated Convolutional Networks/es
- Language Modeling with Gated Convolutional Networks/paper
- Language Modeling with Gated Convolutional Networks/paper/en
- Language Modeling with Gated Convolutional Networks/paper/es
- Language Modeling with Gated Convolutional Networks/paper/zh
- Language Modeling with Gated Convolutional Networks/zh
- Language Models are Few-Shot Learners
- Language Models are Few-Shot Learners/en
- Language Models are Few-Shot Learners/es
- Language Models are Few-Shot Learners/zh
M
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts/en
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts/es
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts/paper
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts/paper/en
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts/paper/es
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts/paper/zh
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts/zh
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan/en
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan/es
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan/paper
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan/paper/en
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan/paper/es
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan/paper/zh
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan/zh
O
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer/en
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer/es
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer/paper
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer/paper/en
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer/paper/es
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer/paper/zh
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer/zh
S
- Searching for Activation Functions
- Searching for Activation Functions/en
- Searching for Activation Functions/es
- Searching for Activation Functions/paper
- Searching for Activation Functions/paper/en
- Searching for Activation Functions/paper/es
- Searching for Activation Functions/paper/zh
- Searching for Activation Functions/zh
W
- Wide & Deep Learning for Recommender Systems
- Wide & Deep Learning for Recommender Systems/en
- Wide & Deep Learning for Recommender Systems/es
- Wide & Deep Learning for Recommender Systems/paper
- Wide & Deep Learning for Recommender Systems/paper/en
- Wide & Deep Learning for Recommender Systems/paper/es
- Wide & Deep Learning for Recommender Systems/paper/zh
- Wide & Deep Learning for Recommender Systems/zh