33 citations · 136 across the 23 of their papers we have counts for
11 papers · 1 filter
Self-Progressing Robust Training
Minhao Cheng, Pin-Yu Chen, Sijia Liu +3
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. Current robust training methods…
Optimizing Mode Connectivity via Neuron Alignment
N. Joseph Tatro, Pin-Yu Chen, Payel Das +3
The loss landscapes of deep neural networks are not well understood due to their high nonconvexity. Empirically, the local minima of these loss functions can be connected by a lear…
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
Pierre L. Dognin, Igor Melnyk, Inkit Padhi +2
In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak s…
Explaining Chemical Toxicity using Missing Features
Kar Wai Lim, Bhanushee Sharma, Payel Das +2
Chemical toxicity prediction using machine learning is important in drug development to reduce repeated animal and human testing, thus saving cost and time. It is highly recommende…
Active learning of deep surrogates for PDEs: Application to metasurface design
Raphaël Pestourie, Youssef Mroueh, Thanh V. Nguyen +2
Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components. However, the training…
Combinatorial Black-Box Optimization with Expert Advice
Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios +4
We consider the problem of black-box function optimization over the boolean hypercube. Despite the vast literature on black-box function optimization over continuous domains, not m…