uber-research / loss-change-allocation
☆61Updated last year
Alternatives and similar repositories for loss-change-allocation:
Users that are interested in loss-change-allocation are comparing it to the libraries listed below
- Computing various norms/measures on over-parametrized neural networks☆49Updated 6 years ago
- [NeurIPS'19] [PyTorch] Adaptive Regularization in NN☆68Updated 5 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆53Updated 5 years ago
- Implementation of Information Dropout☆39Updated 7 years ago
- This repository is no longer maintained. Check☆81Updated 4 years ago
- ☆34Updated 6 years ago
- Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018☆41Updated 6 years ago
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 5 years ago
- TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".☆60Updated 6 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 4 years ago
- ☆26Updated 5 years ago
- Computing the eigenvalues of Neural Tangent Kernel and Conjugate Kernel (aka NNGP kernel) over the boolean cube☆48Updated 5 years ago
- Implementation of iterative inference in deep latent variable models☆43Updated 5 years ago
- Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018☆22Updated 5 years ago
- Implementation of Conditionally Shifted Neurons by Munkhdalai et al. (https://arxiv.org/pdf/1712.09926.pdf)☆28Updated 6 years ago
- ☆26Updated 5 years ago
- Implementation of "Learning with Random Learning Rates" in PyTorch.☆102Updated 5 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆113Updated 6 years ago
- [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling☆42Updated last year
- Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses☆184Updated last year
- A pytorch implementation for the LSTM experiments in the paper: Why Gradient Clipping Accelerates Training: A Theoretical Justification f…☆44Updated 5 years ago
- Code for "Systematic Generalization: What Is Required and Can It Be Learned"