Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
arXiv:2107.03690
Abstract
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.
References in corpus (11)
- A Simple Framework for Contrastive Learning of Visual Representations
- Snorkel: Rapid Training Data Creation with Weak Supervision
- Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
- word2vec Parameter Learning Explained
- Learning the Structure of Generative Models without Labeled Data
- Deep Neural Decision Trees
- Weakly-Supervised Disentanglement Without Compromises
- Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
- Learning Latent Subspaces in Variational Autoencoders
- DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
- Learning Semantic Annotations for Tabular Data