nitarshan / robust-generalization-measures
Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)
☆28Updated 4 years ago
Alternatives and similar repositories for robust-generalization-measures:
Users that are interested in robust-generalization-measures are comparing it to the libraries listed below
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- Computing various measures and generalization bounds on convolutional and fully connected networks☆35Updated 6 years ago
- ☆38Updated 3 years ago
- ☆62Updated 3 years ago
- ☆35Updated last year
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 2 years ago
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated 2 years ago
- ☆55Updated 4 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆41Updated last year
- An Investigation of Why Overparameterization Exacerbates Spurious Correlations☆30Updated 4 years ago
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆68Updated 8 months ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- ☆34Updated 3 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated last year
- SGD with large step sizes learns sparse features [ICML 2023]☆32Updated last year
- CIFAR-5m dataset☆38Updated 4 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 4 years ago
- A way to achieve uniform confidence far away from the training data.☆37Updated 3 years ago
- Predicting Out-of-Distribution Error with the Projection Norm☆17Updated 2 years ago
- Training vision models with full-batch gradient descent and regularization☆38Updated last year
- ☆15Updated last year
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 4 years ago
- Low-variance and unbiased gradient for backpropagation through categorical random variables, with application in variational auto-encoder…☆17Updated 4 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- ☆50Updated last year
- ☆34Updated 11 months ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆22Updated 2 years ago
- The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size☆17Updated 5 years ago
- Winning Solution of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning☆38Updated 3 years ago