14 citations · 36 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
Learning Dependency Structures for Weak Supervision Models
Paroma Varma, Frederic Sala, Ann He +2
Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating thei…
Training Complex Models with Multi-Task Weak Supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon +3
As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker for…
Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification
Frederic Sala, Shahroze Kabir, Guy Van den Broeck +1
After being trained, classifiers must often operate on data that has been corrupted by noise. In this paper, we consider the impact of such noise on the features of binary classifi…