2 code implementations • 22 May 2020 • Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy Tan, James Hedrick, Jason Crain, Aleksandra Mojsilovic
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
1 code implementation • 3 Nov 2020 • Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das
Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery.
no code implementations • 26 Nov 2017 • Igor Melnyk, Cicero Nogueira dos santos, Kahini Wadhawan, Inkit Padhi, Abhishek Kumar
Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes.
no code implementations • 7 Jul 2017 • Cicero Nogueira dos Santos, Kahini Wadhawan, Bo-Wen Zhou
We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning.
no code implementations • 17 Oct 2018 • Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic
Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.
no code implementations • NeurIPS 2018 • Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, William T. Freeman, Gregory Wornell
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 the training data distribution, referred as the \emph{source domain}.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Jatin Ganhotra, Robert Moore, Sachindra Joshi, Kahini Wadhawan
Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations.
no code implementations • 18 Aug 2021 • Kahini Wadhawan, Payel Das, Barbara A. Han, Ilya R. Fischhoff, Adrian C. Castellanos, Arvind Varsani, Kush R. Varshney
Current methods for viral discovery target evolutionarily conserved proteins that accurately identify virus families but remain unable to distinguish the zoonotic potential of newly discovered viruses.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Tom Sercu, Sebastian Gehrmann, Hendrik Strobelt, Payel Das, Inkit Padhi, Cicero dos Santos, Kahini Wadhawan, Vijil Chenthamarakshan
We present the pipeline in an interactive visual tool to enable the exploration of the metrics, analysis of the learned latent space, and selection of the best model for a given task.
no code implementations • 14 Jul 2022 • Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam
In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model.
no code implementations • 1 Jun 2023 • Rahul Madhavan, Rishabh Garg, Kahini Wadhawan, Sameep Mehta
Our experiments show that CFL achieves such a detoxification without much impact on the model perplexity.
1 code implementation • 19 Nov 2023 • Rahul Madhavan, Kahini Wadhawan
Existing methods for the attribute control task in Language Models (LMs) check for the co-occurrence of words in a sentence with the attribute of interest, and control for them.