Alternatives and similar repositories for tf_optimizers

Users that are interested in tf_optimizers are comparing it to the libraries listed below

• 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
• AMLab-Amsterdam / MNF_VBNN
  Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks
  ☆66Updated 5 years ago
• rhaps0dy / convnets-as-gps
  Code for "Deep Convolutional Networks as shallow Gaussian Processes"
  ☆39Updated 6 years ago
• uber-research / loss-change-allocation
  ☆61Updated 2 years ago
• da-molchanov / variance-networks
  Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
  ☆39Updated 5 years ago
• bneyshabur / over-parametrization
  Computing various norms/measures on over-parametrized neural networks
  ☆50Updated 6 years ago
• mblondel / fenchel-young-losses
  Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses
  ☆192Updated 2 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• akosiorek / forge
  Basic experiment framework for tensorflow.
  ☆91Updated 4 years ago
• kimhc6028 / pathnet-pytorch
  PyTorch implementation of PathNet: Evolution Channels Gradient Descent in Super Neural Networks
  ☆80Updated 7 years ago
• ucla-vision / information-dropout
  Implementation of Information Dropout
  ☆39Updated 8 years ago
• kefirski / variational_dropout
  Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
  ☆48Updated 7 years ago
• MaestroGraph / sparse-hyper
  Code for the paper "Learning sparse transformations through backpropagation"
  ☆43Updated 5 years ago
• insperatum / vhe
  The Variational Homoencoder: Learning to learn high capacity generative models from few examples
  ☆34Updated 2 years ago
• david-abel / icml_2019
  Notes for ICML 2019
  ☆38Updated 6 years ago
• joelouismarino / amortized-variational-filtering
  PyTorch implementation of AVF
  ☆45Updated 5 years ago
• yaohungt / Adaptive-Regularization-Neural-Network
  [NeurIPS'19] [PyTorch] Adaptive Regularization in NN
  ☆68Updated 6 years ago
• renmengye / meta-optim-public
  Understanding Short-Horizon Bias in Stochastic Meta-Optimization
  ☆37Updated 7 years ago
• ermongroup / BiasAndGeneralization
  ☆27Updated 6 years ago
• vithursant / VAE-Gumbel-Softmax
  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
• jpeg729 / pytorch_bits
  ☆80Updated 7 years ago
• facebookresearch / modeling_long_term_future
  Code for ICLR 2019 paper Learning Dynamics Model by Incorporating the Long Term Future
  ☆50Updated 6 years ago
• amzn / metalearn-leap
  Original PyTorch implementation of the Leap meta-learner (https://arxiv.org/abs/1812.01054) along with code for running the Omniglot expe…
  ☆148Updated 2 years ago
• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 5 years ago
• myshkov / bnn-analysis
  ☆16Updated 8 years ago
• IraKorshunova / bruno
  a deep recurrent model for exchangeable data
  ☆34Updated 5 years ago
• wittawatj / kernel-gof
  NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
  ☆67Updated 6 years ago
• rizar / systematic-generalization-sqoop
  Code for "Systematic Generalization: What Is Required and Can It Be Learned"
  ☆37Updated 6 years ago
• anandsaha / nips.cocob.pytorch
  PyTorch implementation of the NIPS'17 paper Training Deep Networks without Learning Rates Through Coin Betting.
  ☆38Updated 7 years ago
• asteroidhouse / self-tuning-networks
  Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
  ☆54Updated 6 years ago