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Inkit Padhi
Researcher at IBM
Publications - 43
Citations - 936
Inkit Padhi is an academic researcher from IBM. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 10, co-authored 31 publications receiving 593 citations.
Papers
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Proceedings ArticleDOI
Does String-Based Neural MT Learn Source Syntax?
TL;DR: This work investigates whether a neural, encoderdecoder translation system learns syntactic information on the source side as a by-product of training and proposes two methods to detect whether the encoder has learned local and global source syntax.
Proceedings ArticleDOI
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer.
TL;DR: The authors proposed a method for training encoder-decoders using non-parallel data that combines a collaborative classifier, attention and the cycle consistency loss to translate offensive sentences into non-offensive ones.
Journal ArticleDOI
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.
Payel Das,Payel Das,Tom Sercu,Tom Sercu,Kahini Wadhawan,Inkit Padhi,Sebastian Gehrmann,Sebastian Gehrmann,Flaviu Cipcigan,Vijil Chenthamarakshan,Hendrik Strobelt,Cicero Nogueira dos Santos,Cicero Nogueira dos Santos,Pin-Yu Chen,Yi Yan Yang,Jeremy P. K. Tan,James L. Hedrick,Jason Crain,Jason Crain,Aleksandra Mojsilovic +19 more
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.
Proceedings Article
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan,Payel Das,Samuel C. Hoffman,Hendrik Strobelt,Inkit Padhi,Kar Wai Lim,Benjamin Hoover,Matteo Manica,Jannis Born,Jannis Born,Teodoro Laino,Aleksandra Mojsilovic +11 more
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.
Posted Content
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
TL;DR: GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm, that can direct the generation towards a corresponding condition such as named-entity tag, semantic role label, or sentiment.