134 citations · 701 across the 33 of their papers we have counts for
5 papers · 1 filter
Diagnosing and Rectifying Vision Models using Language
Yuhui Zhang, Jeff Z. HaoChen, Shih-Cheng Huang +3
Recent multi-modal contrastive learning models have demonstrated the ability to learn an embedding space suitable for building strong vision classifiers, by leveraging the rich inf…
DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder, Colby Banbury, Xiaozhe Yao +42
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithf…
Personalized Federated Learning with First Order Model Optimization
Michael Zhang, Karan Sapra, Sanja Fidler +2
While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Her…
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
Sabina Tomkins, Peng Liao, Predrag Klasnja +2
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of indi…
Tackling Over-pruning in Variational Autoencoders
Serena Yeung, Anitha Kannan, Yann Dauphin +1
Variational autoencoders (VAE) are directed generative models that learn factorial latent variables. As noted by Burda et al. (2015), these models exhibit the problem of factor ove…