266 citations · 614 across the 24 of their papers we have counts for
8 papers · 1 filter
Hyperbolic Graph Convolutional Neural Networks
Ines Chami, Rex Ying, Christopher Ré +1
Graph convolutional neural networks (GCNs) embed nodes in a graph into Euclidean space, which has been shown to incur a large distortion when embedding real-world graphs with scale…
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…
Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels
Daniel Y. Fu, Will Crichton, James Hong +7
Many real-world video analysis applications require the ability to identify domain-specific events in video, such as interviews and commercials in TV news broadcasts, or action seq…
PipeMare: Asynchronous Pipeline Parallel DNN Training
Bowen Yang, Jian Zhang, Jonathan Li +3
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware u…
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro +1
Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For e…
Scene Graph Prediction with Limited Labels
Vincent S. Chen, Paroma Varma, Ranjay Krishna +3
Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete…