lucfra / FAR-HOLinks
Gradient based hyperparameter optimization & meta-learning package for TensorFlow
☆188Updated 5 years ago
Alternatives and similar repositories for FAR-HO
Users that are interested in FAR-HO are comparing it to the libraries listed below
Sorting:
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport☆134Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Adaptive Neural Trees☆155Updated 6 years ago
- Hypergradient descent☆149Updated last year
- PyTorch implementation of Neural Processes☆89Updated 6 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆53Updated 6 years ago
- ☆116Updated last year
- ☆124Updated last year
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- a python implementation of various versions of the information bottleneck, including automated parameter searching☆128Updated 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, …☆112Updated 6 years ago
- Collection of algorithms for approximating Fisher Information Matrix for Natural Gradient (and second order method in general)☆140Updated 6 years ago
- Code to reproduce experiments in "Meta-learning probabilistic inference for prediction"☆70Updated 4 years ago
- Library to manage machine learning problems as `Tasks' and to sample from Task distributions. Includes Tensorflow implementation of impli…☆48Updated 3 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 6 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 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
- ☆152Updated 5 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆252Updated 6 years ago
- ☆133Updated 7 years ago
- Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"☆127Updated 6 years ago
- Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)☆205Updated 3 years ago
- hessian in pytorch☆187Updated 4 years ago
- Example code for the paper "Understanding deep learning requires rethinking generalization"☆178Updated 5 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆66Updated 5 years ago
- PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models☆152Updated 5 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Learning kernels to maximize the power of MMD tests☆210Updated 7 years ago
- Implementation of the variational continual learning method☆192Updated 6 years ago