62 citations · 176 across the 28 of their papers we have counts for
5 papers · 1 filter
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Mayee F. Chen, Daniel Y. Fu, Avanika Narayan +4
An ideal learned representation should display transferability and robustness. Supervised contrastive learning (SupCon) is a promising method for training accurate models, but prod…
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…
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Mayee F. Chen, Daniel Y. Fu, Frederic Sala +5
Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We…
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Daniel Y. Fu, Mayee F. Chen, Frederic Sala +3
Weak supervision is a popular method for building machine learning models without relying on ground truth annotations. Instead, it generates probabilistic training labels by estima…
Multi-Resolution Weak Supervision for Sequential Data
Frederic Sala, Paroma Varma, Jason Fries +8
Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. Howeve…