[1]: deep2reproduce 2019 Fall - 1Analysis papers
read on: - 12 Dec 2019analysis generalization forgetting training optimization subspace informax normalization Sample-selection
| Team INDEX | Title & Link | Tags | Our Slide |
|---|---|---|---|
| T2 | Empirical Study of Example Forgetting During Deep Neural Network Learning | Sample Selection, forgetting | OurSlide |
| T29 | Select Via Proxy: Efficient Data Selection For Training Deep Networks | Sample Selection | OurSlide |
| T9 | How SGD Selects the Global Minima in over-parameterized Learning | optimization | OurSlide |
| T10 | Escaping Saddles with Stochastic Gradients | optimization | OurSlide |
| T13 | To What Extent Do Different Neural Networks Learn the Same Representation | subspace | OurSlide |
| T19 | On the Information Bottleneck Theory of Deep Learning | informax | OurSlide |
| T20 | Visualizing the Loss Landscape of Neural Nets | normalization | OurSlide |
| T21 | Using Pre-Training Can Improve Model Robustness and Uncertainty | training, analysis | OurSlide |
| T24 | Norm matters: efficient and accurate normalization schemes in deep networks | normalization | OurSlide |