Alternatives and similar repositories for information-bottleneck

Users that are interested in information-bottleneck are comparing it to the libraries listed below

• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 7 years ago
• authors-1901-10912 / A-Meta-Transfer-Objective-For-Learning-To-Disentangle-Causal-Mechanisms
  Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"
  ☆127Updated 6 years ago
• wiseodd / natural-gradients
  Collection of algorithms for approximating Fisher Information Matrix for Natural Gradient (and second order method in general)
  ☆141Updated 6 years ago
• AMLab-Amsterdam / MNF_VBNN
  Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks
  ☆66Updated 5 years ago
• SeojinBang / VIBI
  Explaining a black-box using Deep Variational Information Bottleneck Approach
  ☆46Updated 2 years ago
• artemyk / ibsgd
  ☆154Updated 5 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 5 years ago
• ec6dde01667145e58de60f864e05a4 / CausalOptimizationAnon
  ☆65Updated last year
• ruosongwang / CNTK
  Convolutional Neural Tangent Kernel
  ☆112Updated 6 years ago
• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆104Updated 5 years ago
• thegregyang / NNspectra
  Computing the eigenvalues of Neural Tangent Kernel and Conjugate Kernel (aka NNGP kernel) over the boolean cube
  ☆47Updated 6 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 7 years ago
• asteroidhouse / self-tuning-networks
  Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
  ☆54Updated 6 years ago
• ucla-vision / information-dropout
  Implementation of Information Dropout
  ☆39Updated 8 years ago
• ganguli-lab / deepchaos
  Experiments for the paper "Exponential expressivity in deep neural networks through transient chaos"
  ☆73Updated 9 years ago
• bneyshabur / over-parametrization
  Computing various norms/measures on over-parametrized neural networks
  ☆50Updated 6 years ago
• fwcore / mean-field-theory-deep-learning
  paper lists and information on mean-field theory of deep learning
  ☆78Updated 6 years ago
• ravidziv / IDNNs
  explore DNNs via Infomration
  ☆265Updated 5 years ago
• alexalemi / vib_demo
  ☆91Updated 3 years ago
• emtiyaz / vadam
  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
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆54Updated last year
• pomonam / NoisyNaturalGradient
  TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".
  ☆60Updated 6 years ago
• anirudh9119 / RIMs
  Code for "Recurrent Independent Mechanisms"
  ☆118Updated 3 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• gbaydin / hypergradient-descent
  Hypergradient descent
  ☆148Updated last year
• abdulfatir / normalizing-flows
  Understanding normalizing flows
  ☆132Updated 5 years ago
• kefirski / variational_dropout
  Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
  ☆48Updated 7 years ago
• wlwkgus / NoisyNaturalGradient
  Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"
  ☆121Updated 7 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 years ago