Alternatives and similar repositories for icml17_knn:

Users that are interested in icml17_knn are comparing it to the libraries listed below

• geyang / grammar_variational_autoencoder
  pytorch implementation of grammar variational autoencoder
  ☆61Updated 6 years ago
• hechtlinger / graph_cnn
  Supplementary code to "Convolutional Neural Networks Generalization Utilizing the Data Graph Structure"
  ☆51Updated 7 years ago
• pfnet-research / bayesgrad
  BayesGrad: Explaining Predictions of Graph Convolutional Networks
  ☆63Updated 3 years ago
• PrincetonLIPS / reversible-inductive-construction
  Discrete Object Generation with Reversible Inductive Construction (NeurIPS 2019)
  ☆30Updated 4 years ago
• MaestroGraph / sparse-hyper
  Code for the paper "Learning sparse transformations through backpropagation"
  ☆43Updated 5 years ago
• tdozat / Optimization
  ☆24Updated 8 years ago
• horacepan / MLGkernel
  Implementation of the Multiscale Laplacian Graph Kernel
  ☆18Updated 5 years ago
• vsuriya93 / Deep-Learning-for-Graph-Representations
  This contains my M.Tech project work on using Deep Leanring for learning graph representations. Data will be provided on request
  ☆33Updated 8 years ago
• rdevon / BGAN
  boundary-seeking generative adversarial networks
  ☆46Updated 7 years ago
• graphreason / graphreason.github.io
  Learning and Reasoning with Graph-Structured Data (ICML 2019 Workshop)
  ☆26Updated 5 years ago
• horacepan / gnns
  Collection of graph neural networks in pytorch
  ☆50Updated 7 years ago
• danieldjohnson / gated-graph-transformer-network
  Code to accompany the paper "Learning Graphical State Transitions"
  ☆170Updated 7 years ago
• jcatw / scnn
  Implementation of search-convolutional neural networks (SCNNs)
  ☆51Updated 8 years ago
• msobroza / SparsemaxPytorch
  SparseMax activation function implementation (ICML 2016) (PyTorch)
  ☆27Updated 7 years ago
• wlwkgus / GibbsNet
  Implementation of paper "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models" in PyTorch
  ☆57Updated 7 years ago
• Cyanogenoid / perm-optim
  [ICLR 2019] Learning Representations of Sets through Optimized Permutations
  ☆36Updated 6 years ago
• ml-lab / auxiliary-deep-generative-models
  Implementation of auxiliary deep generative models for semi-supervised learning
  ☆28Updated 9 years ago
• jaak-s / nips2014-survey
  Survey of hyperparameter optimization use in NIPS2014
  ☆12Updated 9 years ago
• vermaMachineLearning / FGSD
  Graph Feature Representation/Vector Based On The Family Of Graph Spectral Distances (NIPS 2017).
  ☆24Updated 5 years ago
• sidaw / fastdropout
  code for fast dropout
  ☆46Updated 9 years ago
• IBM / IPC-graph-data
  IPC: A Graph Data Set Compiled from International Planning Competitions
  ☆45Updated 5 years ago
• RuiShu / tensorbayes
  Deep variational inference in tensorflow
  ☆56Updated 7 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
• tjanez / PyMTL
  PyMTL (Python library for Multi-task learning) is a Python module implementing a Multi-task learning framework built on top of scikit-lea…
  ☆44Updated 11 years ago
• amardt / DeepGenMSM
  Code for the results of the Paper:
  ☆21Updated 6 years ago
• trangptm / HighwayNetwork
  For training very deep networks
  ☆10Updated 7 years ago
• yaoli / nade_k
  An iterative neural autoregressive distribution estimator (NADE-K)
  ☆26Updated 10 years ago
• Hanjun-Dai / sdvae
  code for Syntax-Directed Variational Autoencoder that generates programs and molecues
  ☆78Updated 6 years ago
• wittawatj / interpretable-test
  NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
  ☆63Updated 6 years ago
• google / joint_vae
  Code to build VAE models that are jointly conditioned.
  ☆36Updated 7 years ago