| No. | Read Date | Title and Information | We Read @ |
|---|---|---|---|
| 1 | 2020, May, 5 | Optimization and New Loss in Deep Learning | 2020-W7 |
| 2 | 2017, Nov, 9 | Optimization IV - change DNN architecture for Optimization | 2017-W12 |
| 3 | 2017, Nov, 7 | Optimization III - Optimization for DNN | 2017-W12 |
| 4 | 2017, Nov, 2 | Optimization II - DNN for Optimization | 2017-W11 |
| 5 | 2017, Oct, 31 | Optimization I - Understanding DNN Optimization | 2017-W11 |
| 6 | 2017, Apr, 22 | Optimization17- Optimization in DNN | 2017-team |
| Index | Papers | Our Slides |
|---|---|---|
| 1 | Review on Semi-Supervised Learning | Zhe Survey |
| 2 | Review on Generative Adversarial Networks | Zhe Survey |
| 3 | Information theory in deep learning | Zhe Survey |
| 4 | Lagrange Optimization | Zhe Survey |
| 5 | Deep Learning and Information Theory, and Graph Neural Network | Derrick Survey |
| 6 | Loss Functions for Deep Structured Models | Jack Survey |
| 7 | Group Sparsity and Optimization | Zhe Survey |
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Shijia | Professor Forcing: A New Algorithm for Training Recurrent Networks, 1 NIPS16 | PDF + Video | |
| Beilun+Arshdeep | Mollifying Networks, Bengio, ICLR17 2 | PDF / PDF2 |
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17 1 | ||
| Chaojiang | Adaptive Neural Networks for Efficient Inference, ICML17 2 | ||
| Bargav | Practical Gauss-Newton Optimisation for Deep Learning, ICML17 3 | ||
| Rita | How to Escape Saddle Points Efficiently, ICML17 4 | ||
| Batched High-dimensional Bayesian Optimization via Structural Kernel Learning |
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| GaoJi | Neural Architecture Search with Reinforcement Learning, ICLR17 1 | ||
| Ceyer | Learning to learn 2 | DLSS17video | |
| Beilun | Optimization as a Model for Few-Shot Learning, ICLR17 3 | PDF + More | |
| Anant | Neural Optimizer Search with Reinforcement Learning, ICML17 4 |
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Ceyer | An overview of gradient optimization algorithms, 1 | ||
| Shijia | Osborne - Probabilistic numerics for deep learning 2 | DLSS 2017 + Video | PDF / PDF2 |
| Jack | Automated Curriculum Learning for Neural Networks, ICML17 3 | ||
| DLSS17 | Johnson - Automatic Differentiation 4 | slide + video |
| Presenter | Papers | Paper URL | Our Slides |
|---|---|---|---|
| Muthu | Optimization Methods for Large-Scale Machine Learning, Léon Bottou, Frank E. Curtis, Jorge Nocedal 1 | ||
| Muthu | Fast Training of Recurrent Networks Based on EM Algorithm (1998) 2 | ||
| Muthu | FitNets: Hints for Thin Deep Nets, ICLR15 3 | ||
| Muthu | Two NIPS 2015 Deep Learning Optimization Papers | ||
| Muthu | Difference Target Propagation (2015) 4 |
Index Papers Our Slides 1 Review on Semi-Supervised Learning Zhe Survey 2 ...
Presenter Papers Paper URL Our Slides Shijia Professor Forcing: A New Algorithm for Training Rec...
Presenter Papers Paper URL Our Slides GaoJi Forward and Reverse Gradient-Based Hyperparameter Op...
Presenter Papers Paper URL Our Slides GaoJi Neural Architecture Search with Reinforcement Learni...
Presenter Papers Paper URL Our Slides Ceyer An overview of gradient optimization algorithms, 1 ...
Presenter Papers Paper URL Our Slides Muthu Optimization Methods for Large-Scale Machine Learnin...