Alternatives and similar repositories for What-Can-Neural-Networks-Reason-About

Users that are interested in What-Can-Neural-Networks-Reason-About are comparing it to the libraries listed below

• oskopek / mvae
  Mixed-curvature Variational Autoencoders (ICLR 2020)
  ☆62Updated 4 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• yichousun / Winter2021_CS249_GNN
  ☆54Updated 2 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• Cyanogenoid / fspool
  [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling
  ☆42Updated last year
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 5 years ago
• ec6dde01667145e58de60f864e05a4 / CausalOptimizationAnon
  ☆65Updated 11 months ago
• ermongroup / subsets
  Code for Reparameterizable Subset Sampling via Continuous Relaxations, IJCAI 2019.
  ☆57Updated last year
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 5 years ago
• ARM-gradient / ARSM
  Low-variance and unbiased gradient for backpropagation through categorical random variables, with application in variational auto-encoder…
  ☆17Updated 4 years ago
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆104Updated 5 years ago
• wouterkool / estimating-gradients-without-replacement
  Estimating Gradients for Discrete Random Variables by Sampling without Replacement
  ☆40Updated 5 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 5 years ago
• hadarser / SetToGraphPaper
  SetToGraph paper repository
  ☆22Updated 4 years ago
• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆56Updated 3 years ago
• PurdueMINDS / Equivalence
  Equivalence Between Structural Representations and Positional Node Embeddings
  ☆22Updated 5 years ago
• PurdueMINDS / JanossyPooling
  ☆26Updated 6 years ago
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆37Updated last year
• xinshi-chen / l2stop
  Learning To Stop While Learning To Predict
  ☆34Updated 2 years ago
• lfbo-ml / lfbo
  Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022
  ☆44Updated 2 years ago
• choidami / sst
  ☆49Updated 4 years ago
• LingxiaoShawn / PairNorm
  Source code for PairNorm (ICLR 2020)
  ☆78Updated 5 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆52Updated 5 years ago
• gbarcik / graphnets
  Extending the Neural Graph Algorithm Executor
  ☆13Updated 2 years ago
• JiaxuanYou / G2SAT
  G2SAT: Learning to Generate SAT Formulas
  ☆49Updated 5 years ago
• gblackout / NLIL
  Neural Logic Inductive Learning
  ☆44Updated 2 years ago
• Yujun-Yan / Neural-Execution-Engines
  Code for Neural Execution Engines: Learning to Execute Subroutines
  ☆17Updated 4 years ago
• mingzhang-yin / ARM-gradient
  Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
  ☆28Updated 6 years ago
• yixinwang / representation-causal-public
  ☆44Updated 3 years ago
• nkeriven / random-graph-gnn
  ☆12Updated 4 years ago