Alternatives and similar repositories for kdd-2022-multimodal-automl-tutorial

Users that are interested in kdd-2022-multimodal-automl-tutorial are comparing it to the libraries listed below

• JeanKaddour / SIN
  Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)
  ☆42Updated 3 years ago
• epfl-lts2 / GraphMining-TheWebConf2021
  ☆14Updated 4 years ago
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆37Updated last year
• moorecsys / moorecsys.github.io
  Tutorial on Multi-Objective Recommender Systems @ KDD 2021
  ☆19Updated 2 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• DeepGraphLearning / SPN
  ☆29Updated 2 years ago
• lethaiq / GRACE_KDD20
  GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon …
  ☆21Updated 4 years ago
• kakaobrain / irm-empirical-study
  An Empirical Study of Invariant Risk Minimization
  ☆27Updated 4 years ago
• ninghaohello / Interpretation2Adversary
  Adversarial learning by utilizing model interpretation
  ☆10Updated 6 years ago
• yang-han / P-reg
  Rethinking Graph Regularization for Graph Neural Networks (AAAI2021)
  ☆34Updated 4 years ago
• StatsDLMathsRecomSys / Self-attention-with-Functional-Time-Representation-Learning
  ☆50Updated 2 years ago
• sxjscience / automl_multimodal_benchmark
  Repository for Multimodal AutoML Benchmark
  ☆66Updated 3 years ago
• LARS-research / TabGNN
  ☆51Updated 3 years ago
• flyingtango / DiGCL
  The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
  ☆36Updated 3 years ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆23Updated 4 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• DAMO-DI-ML / KDD2022-Tutorial-Time-Series
  KDD'22 Tutorial: Robust Time Series Analysis and Applications An Industrial Perspective
  ☆32Updated last year
• lightlightdyy / Deep-Learning-and-Causal-Inference
  ☆29Updated 6 years ago
• ahujak / IB-IRM
  ☆30Updated 3 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• iesl / geometric-graph-embedding
  ☆13Updated 7 months ago
• minjiyoon / CGT
  Scalable and privacy-enhanced graph generative models for benchmark graph neural networks
  ☆17Updated last year
• CODE-SUBMIT / Graph_Neighborhood_1
  First and Complementary Neighborhood Combination of Adjacency Matrix for Graph Learning
  ☆20Updated last year
• twitter-research / sign
  SIGN: Scalable Inception Graph Network
  ☆96Updated 4 years ago
• mwcvitkovic / Supervised-Learning-on-Relational-Databases-with-GNNs
  Code to reproduce the results in the paper Supervised Learning on Relational Databases with Graph Neural Networks.
  ☆63Updated 5 years ago
• razhangwei / CAUSE
  ☆30Updated 3 years ago
• IirisSundin / active-learning-for-decision-making
  This repository contains the code used in a publication 'Active Learning for Decision-Making from Imbalanced Observational Data', Iiris S…
  ☆11Updated 6 years ago
• cyber-meow / PUbiasedN
  PyTorch implementation for the paper Classification from Positive, Unlabeled and Biased Negative Data.
  ☆19Updated last year
• nikhil-dce / SBM-meet-GNN
  Implementation of SBM-meet-GNN
  ☆23Updated 6 years ago
• trevor-richardson / MCFlow
  Monte Carlo Flow Models for Data Imputation
  ☆18Updated 5 years ago