mbp28 / determinantal-point-processes

Related projects ⓘ

Alternatives and complementary repositories for determinantal-point-processes

• rhaps0dy / convnets-as-gps
  Code for "Deep Convolutional Networks as shallow Gaussian Processes"
  ☆38Updated 5 years ago
• rdevon / BGAN
  boundary-seeking generative adversarial networks
  ☆46Updated 6 years ago
• wittawatj / kernel-gof
  NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
  ☆67Updated 5 years ago
• AMLab-Amsterdam / MNF_VBNN
  Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks
  ☆64Updated 4 years ago
• insperatum / vhe
  The Variational Homoencoder: Learning to learn high capacity generative models from few examples
  ☆33Updated last year
• sordonia / zforcing
  ZForcing Repo
  ☆40Updated 6 years ago
• renmengye / meta-optim-public
  Understanding Short-Horizon Bias in Stochastic Meta-Optimization
  ☆37Updated 6 years ago
• javiergonzalezh / dpp
  Python package to sample from determinantal point processes
  ☆18Updated 9 years ago
• gyglim / dvn
  Reference implementation for Structured Prediction with Deep Value Networks
  ☆55Updated 7 years ago
• pomonam / NoisyNaturalGradient
  TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".
  ☆60Updated 5 years ago
• lorraine2 / hypernet-hypertraining
  Code for Stochastic Hyperparameter Optimization through Hypernetworks
  ☆23Updated 6 years ago
• siddharth-agrawal / Generative-Moment-Matching-Networks
  ☆64Updated 8 years ago
• AMLab-Amsterdam / SEVDL_MGP
  Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…
  ☆31Updated 4 years ago
• joelouismarino / iterative_inference
  Implementation of iterative inference in deep latent variable models
  ☆43Updated 5 years ago
• wlwkgus / NoisyNaturalGradient
  Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"
  ☆121Updated 6 years ago
• wlwkgus / GibbsNet
  Implementation of paper "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models" in PyTorch
  ☆58Updated 6 years ago
• zhegan27 / Bayesian_RNN
  The code for the ACL 2017 paper "Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling"
  ☆29Updated 7 years ago
• lrjconan / RBP
  Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018
  ☆39Updated 5 years ago
• greentfrapp / cond-shift-neurons
  Implementation of Conditionally Shifted Neurons by Munkhdalai et al. (https://arxiv.org/pdf/1712.09926.pdf)
  ☆29Updated 6 years ago
• kefirski / variational_dropout
  Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
  ☆50Updated 7 years ago
• lrjconan / deep_parsimonious
  Learning Deep Parsimonious Representations, Deep Learning, Clustering, NIPS 2016
  ☆14Updated 4 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated last year
• YixuanLi / convergent_learning
  Code for paper "Convergent Learning: Do different neural networks learn the same representations?"
  ☆85Updated 8 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆74Updated 4 years ago
• artix41 / AVO-pytorch
  Implementation of Adversarial Variational Optimization in PyTorch
  ☆43Updated 6 years ago
• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 4 years ago
• Cyanogenoid / perm-optim
  [ICLR 2019] Learning Representations of Sets through Optimized Permutations
  ☆36Updated 5 years ago
• wittawatj / interpretable-test
  NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
  ☆63Updated 6 years ago