activity
20172021
most citedLearning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks

24 citations · 40 across the 3 of their papers we have counts for

collaborators

7 papers

q-bio.QM20216 cited

Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention

Kahini Wadhawan, Payel Das, Barbara A. Han +4

Current methods for viral discovery target evolutionarily conserved proteins that accurately identify virus families but remain unable to distinguish the zoonotic potential of newl…

cs.CL2020

Effects of Naturalistic Variation in Goal-Oriented Dialog

Jatin Ganhotra, Robert Moore, Sachindra Joshi +1

Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations. Most datasets are c…

cs.LG2020

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics

Payel Das, Tom Sercu, Kahini Wadhawan +12

De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled La…

cs.LG2018

Co-regularized Alignment for Unsupervised Domain Adaptation

Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan +4

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from t…

q-bio.QM2018

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

Payel Das, Kahini Wadhawan, Oscar Chang +6

Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework Pep…

cs.CL201710 cited

Improved Neural Text Attribute Transfer with Non-parallel Data

Igor Melnyk, Cicero Nogueira dos Santos, Kahini Wadhawan +2

Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes. In this work, we propose multiple improvemen…