ron-amit / meta-learning-adjusting-priorsLinks
Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018
☆22Updated 6 years ago
Alternatives and similar repositories for meta-learning-adjusting-priors
Users that are interested in meta-learning-adjusting-priors are comparing it to the libraries listed below
Sorting:
- Computing various norms/measures on over-parametrized neural networks☆50Updated 7 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 7 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 5 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆60Updated 6 years ago
- Code to reproduce experiments in "Meta-learning probabilistic inference for prediction"☆69Updated 4 years ago
- ☆14Updated 7 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆42Updated 3 years ago
- Implementation of iterative inference in deep latent variable models☆43Updated 6 years ago
- PyTorch Implementation of Neural Statistician☆61Updated 3 years ago
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 5 years ago
- Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"☆121Updated 7 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆27Updated 6 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆36Updated 5 years ago
- Code for Unsupervised Learning via Meta-Learning.☆123Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆102Updated 7 years ago
- This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):☆54Updated 8 months ago
- This repository contains implementations of the paper, Bayesian Model-Agnostic Meta-Learning.☆61Updated 6 years ago
- TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".☆60Updated 7 years ago
- ☆43Updated 7 years ago
- Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018☆43Updated 7 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆43Updated 4 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆91Updated 5 years ago
- Code for Stochastic Hyperparameter Optimization through Hypernetworks☆28Updated 7 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆54Updated last year
- Official PyTorch code release for Implicit Gradient Transport, NeurIPS'19☆21Updated 6 years ago
- A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"☆20Updated 7 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 3 years ago
- An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU…☆72Updated 7 years ago