463 citations · 5.1k across the 190 of their papers we have counts for
195 papers
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Vinay Kumar Verma, Nikhil Mehta, Shijing Si +2
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, the…
Pseudo-OOD training for robust language models
Dhanasekar Sundararaman, Nikhil Mehta, Lawrence Carin
While pre-trained large-scale deep models have garnered attention as an important topic for many downstream natural language processing (NLP) tasks, such models often make unreliab…
Collaborative Anomaly Detection
Ke Bai, Aonan Zhang, Zhizhong Li +3
In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item. This can be formula…
Improving Downstream Task Performance by Treating Numbers as Entities
Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang +2
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not a…
elBERto: Self-supervised Commonsense Learning for Question Answering
Xunlin Zhan, Yuan Li, Xiao Dong +3
Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context. Typically, existing approaches first retrieve external evide…
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations
Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin +2
End-to-end learning of dynamical systems with black-box models, such as neural ordinary differential equations (ODEs), provides a flexible framework for learning dynamics from data…