2.3k citations · 2.3k across the 8 of their papers we have counts for
3 papers · 1 filter
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang +5
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each inst…
Metadata Normalization
Mandy Lu, Qingyu Zhao, Jiequan Zhang +4
Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these tech…