ron-amit / meta-learning-adjusting-priors2
Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018
☆18Updated 4 years ago
Alternatives and similar repositories for meta-learning-adjusting-priors2
Users that are interested in meta-learning-adjusting-priors2 are comparing it to the libraries listed below
Sorting:
- 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
- Limitations of the Empirical Fisher Approximation☆47Updated 2 months ago
- Computing various norms/measures on over-parametrized neural networks☆49Updated 6 years ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Geometric Certifications of Neural Nets☆41Updated 2 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Code for "Training Deep Energy-Based Models with f-Divergence Minimization" ICML 2020☆36Updated 2 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Code for Augment & Reduce, a scalable stochastic algorithm for large categorical distributions☆10Updated 7 years ago
- Implementation of the Functional Neural Process models☆43Updated 4 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Code for Stochastic Hyperparameter Optimization through Hypernetworks☆23Updated 6 years ago
- Monotone operator equilibrium networks☆52Updated 4 years ago
- ☆37Updated 5 years ago
- Code for "Accelerating Natural Gradient with Higher-Order Invariance"☆30Updated 5 years ago
- Large-batch Training, Neural Network Optimization☆9Updated 5 years ago
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆33Updated 2 years ago
- Code release for the ICLR paper☆20Updated 6 years ago
- Implementation of Information Dropout☆39Updated 7 years ago
- A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"☆21Updated 6 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆53Updated 5 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 5 years ago
- ☆15Updated 5 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆28Updated 6 years ago
- TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".☆60Updated 6 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆35Updated 4 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- Source code for paper Conservative Uncertainty Estimation By Fitting Prior Networks (ICLR 2020)☆21Updated 2 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- ☆53Updated 9 months ago