yaodongyu / Rethink-BiasVariance-TradeoffLinks
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
☆49Updated 4 years ago
Alternatives and similar repositories for Rethink-BiasVariance-Tradeoff
Users that are interested in Rethink-BiasVariance-Tradeoff are comparing it to the libraries listed below
Sorting:
- A Closer Look at Accuracy vs. Robustness☆89Updated 4 years ago
- ☆55Updated 4 years ago
- Last-layer Laplace approximation code examples☆82Updated 3 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆61Updated 4 years ago
- CIFAR-5m dataset☆39Updated 4 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆89Updated 5 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆49Updated 4 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- ☆58Updated 2 years ago
- Robust Out-of-distribution Detection in Neural Networks☆73Updated 3 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- Code for "The Intrinsic Dimension of Images and Its Impact on Learning" - ICLR 2021 Spotlight https://openreview.net/forum?id=XJk19XzGq2J☆69Updated last year
- Code to reproduce experiments from 'Does Knowledge Distillation Really Work' a paper which appeared in the 2021 NeurIPS proceedings.☆33Updated last year
- Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020☆50Updated 4 years ago
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 3 years ago
- ☆67Updated 6 years ago
- Code to implement the AND-mask and geometric mean to do gradient based optimization, from the paper "Learning explanations that are hard …☆39Updated 4 years ago
- Winning Solution of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning☆40Updated 4 years ago
- ☆40Updated 5 years ago
- Computing various measures and generalization bounds on convolutional and fully connected networks☆35Updated 6 years ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- A way to achieve uniform confidence far away from the training data.☆38Updated 4 years ago
- ☆42Updated 6 years ago
- ☆35Updated last year
- Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).☆56Updated 5 years ago
- Code for the paper: "Tensor Programs II: Neural Tangent Kernel for Any Architecture"☆105Updated 4 years ago
- Randomized Smoothing of All Shapes and Sizes (ICML 2020).☆52Updated 4 years ago
- ☆63Updated 4 years ago
- Geometric Certifications of Neural Nets☆42Updated 2 years ago