xpuoxford / L2G-neurips2021

Related projects ⓘ

Alternatives and complementary repositories for L2G-neurips2021

• DeepGraphLearning / ContinuousGNN
  ☆44Updated 3 years ago
• zhangzx-sjtu / LANTERN-NeurIPS-2019
  Source code for NeurIPS 2019 paper "Learning Latent Processes from High-Dimensional Event Sequences via Efficient Sampling""
  ☆10Updated 3 years ago
• yangalan123 / anhp-andtt
  Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)
  ☆55Updated last month
• wanyu-lin / ICML2021-Gem
  Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
  ☆65Updated 2 years ago
• LingxiaoShawn / PairNorm
  Source code for PairNorm (ICLR 2020)
  ☆76Updated 4 years ago
• xiangwang1223 / reinforced_causal_explainer
  Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022
  ☆32Updated 2 years ago
• shchur / triangular-tpp
  Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)
  ☆23Updated 11 months ago
• rollingstonezz / SGMP_code
  NeurIPS 2021 paper 'Representation Learning on Spatial Networks' code
  ☆17Updated 3 years ago
• weirayao / leap
  LEAP is a tool for discovering latent temporal causal relations with gradient-based neural network.
  ☆34Updated 2 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆23Updated 4 years ago
• calvin-zcx / ndcn
  Neural Dynamics on Complex Networks
  ☆51Updated 4 years ago
• diningphil / graph-mixture-density-networks
  Official Repository of "Graph Mixture Density Networks" (ICML 2021)
  ☆26Updated 2 years ago
• seedatnabeel / TE-CDE
  Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)
  ☆25Updated 2 years ago
• shchur / ifl-tpp
  Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)
  ☆81Updated 3 years ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆22Updated 4 years ago
• KarolisMart / DropGNN
  The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)
  ☆24Updated 2 years ago
• balcilar / gnn-spectral-expressive-power
  Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021
  ☆45Updated 3 years ago
• FFTYYY / TWIRLS
  The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".
  ☆43Updated 3 years ago
• HerreraKrachTeichmann / NJODE
  code for "Neural Jump Ordinary Differential Equations"
  ☆27Updated last year
• PurdueMINDS / RelationalPooling
  ☆35Updated 5 years ago
• marblet / GCNmf
  PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"
  ☆47Updated 9 months ago
• stadlmax / Graph-Posterior-Network
  Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
  ☆40Updated 2 years ago
• networkslab / rnn_flow
  ☆22Updated 3 years ago
• abiodun-ayodeji / Structure-Learning-from-Time-series-Data-with-CausalNex
  Time series data structure learning with NOTEARS and DYNOTEARS
  ☆11Updated 5 months ago
• PurdueMINDS / size-invariant-GNNs
  Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)
  ☆22Updated last year
• ZijieH / LG-ODE
  ☆34Updated 2 years ago
• NNReasoning / What-Can-Neural-Networks-Reason-About
  ☆45Updated 4 years ago
• yh-yao / InterpretableClustering
  ☆21Updated 5 months ago
• yangalan123 / TemporalPointProcessPapers
  Paper lists for Temporal Point Process
  ☆102Updated last month
• Hongrui24 / NeuralHawkesPytorch
  ☆19Updated 4 years ago