mlsanityData to 7 Oct 2026

Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)

arXiv 2107.03690 ↗First posted 8 Jul 2021cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction with observable (unlabeled) data. In total, 15 papers were accepted. All the accepted contributions are listed in these Proceedings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 50 of its 94 references that are in the map. First posted on arXiv 8 Jul 2021.

Builds on

50 of 94 references.

References · 50

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  5. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  7. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  8. 2020Supervised Contrastive Learningcited by 2,803
  9. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
  10. 2016Deep Variational Information Bottleneckcited by 915
  11. 2016Wide & Deep Learning for Recommender Systemscited by 745
  12. 2019TabNet: Attentive Interpretable Tabular Learningcited by 640
38 more
  1. 2018Disentangling by Factorisingcited by 614
  2. 2016Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learningcited by 441
  3. 2017Deep & Cross Network for Ad Click Predictionscited by 392
  4. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 370
  5. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 346
  6. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 319
  7. 2019Label Propagation for Deep Semi-supervised Learningcited by 287
  8. 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
  9. 2020Weakly-Supervised Disentanglement Without Compromisescited by 237
  10. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  11. 2020Contrastive Learning of General-Purpose Audio Representationscited by 168
  12. 2018Isolating Sources of Disentanglement in Variational Autoencoderscited by 167
  13. 2017DARLA: Improving Zero-Shot Transfer in Reinforcement Learningcited by 147
  14. 2019On the Fairness of Disentangled Representationscited by 144
  15. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
  16. 2018Training Complex Models with Multi-Task Weak Supervisioncited by 106
  17. 2019One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizerscited by 102
  18. 2017Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observationscited by 100
  19. 2019Are Disentangled Representations Helpful for Abstract Visual Reasoning?cited by 100
  20. 2020Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methodscited by 98
  21. 2020From ImageNet to Image Classification: Contextualizing Progress on Benchmarkscited by 90
  22. 2019Weakly Supervised Disentanglement with Guaranteescited by 82
  23. 2018Deep Neural Decision Treescited by 72
  24. 2016AdaNet: Adaptive Structural Learning of Artificial Neural Networkscited by 54
  25. 2014word2vec Parameter Learning Explainedcited by 53
  26. 2018Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scalecited by 52
  27. 2020Interactive Weak Supervision: Learning Useful Heuristics for Data Labelingcited by 52
  28. 2018Learning Latent Subspaces in Variational Autoencoderscited by 51
  29. 2017Learning the Structure of Generative Models without Labeled Datacited by 50
  30. 2019Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrievalcited by 37
  31. 2019Learning Dependency Structures for Weak Supervision Modelscited by 32
  32. 2020Unsupervised Contrastive Learning of Sound Event Representationscited by 32
  33. 2015Artificial Neural Networks Applied to Taxi Destination Predictioncited by 18
  34. 2017Inferring Generative Model Structure with Static Analysiscited by 18
  35. 2019Learning Semantic Annotations for Tabular Datacited by 17
  36. 2018DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Predictioncited by 9
  37. 2015On model misspecification and KL separation for Gaussian graphical modelscited by 4
  38. 2017Unsupervised Feature Learning for Audio Analysiscited by 3

Built on by

Not cited yet.