Vijil Chenthamarakshan

TL;DR: In this paper, the authors introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in decision making.

TL;DR: PROSPECT, a decision support tool to help screeners shortlist resumes efficiently is presented and it is shown that extracted information improves the ranking there by making screening task simpler and more efficient.

TL;DR: In this article, a computational method leveraging deep learning and molecular dynamics simulations enables the rapid discovery of antimicrobial peptides with low toxicity and with high potency against diverse Gram-positive and Gram-negative pathogens.

TL;DR: A deep learning based generative modeling framework to design drug candidates specific to a given target protein sequence with high off-target selectivity is presented, and an in silico screening process that accounts for toxicity is augmented to lower the failure rate of the generated drug candidates in later stages of the drug development pipeline.

TL;DR: Experimental results show that the proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less data.