190 citations · 274 across the 15 of their papers we have counts for
19 papers
Behavioral Compositionality of Sparse Autoencoder Representations
Haotian Ma, Ziye Mei, Frederic Sala
Sparse autoencoders (SAEs) represent model activations with sparse, linearly decoded features, but the downstream effects of those features need not compose additively. We introduc…
Domain Generalization via Nuclear Norm Regularization
Zhenmei Shi, Yifei Ming, Ying Fan +2
The ability to generalize to unseen domains is crucial for machine learning systems deployed in the real world, especially when we only have data from limited training domains. In…
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
Nicholas Roberts, Xintong Li, Tzu-Heng Huang +7
Weak supervision (WS) is a powerful method to build labeled datasets for training supervised models in the face of little-to-no labeled data. It replaces hand-labeling data with ag…
Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision
Mayee F. Chen, Daniel Y. Fu, Dyah Adila +4
Foundation models offer an exciting new paradigm for constructing models with out-of-the-box embeddings and a few labeled examples. However, it is not clear how to best apply found…
Generative Modeling Helps Weak Supervision (and Vice Versa)
Benedikt Boecking, Nicholas Roberts, Willie Neiswanger +3
Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To…
Universalizing Weak Supervision
Changho Shin, Winfred Li, Harit Vishwakarma +2
Weak supervision (WS) frameworks are a popular way to bypass hand-labeling large datasets for training data-hungry models. These approaches synthesize multiple noisy but cheaply-ac…