33 citations · 212 across the 29 of their papers we have counts for
7 papers · 1 filter
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models
Pierre L. Dognin, Inkit Padhi, Igor Melnyk +1
Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In thi…
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…
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Yue Cao, Payel Das, Vijil Chenthamarakshan +3
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering. Challenges exist due to the complex sequence--fold…
Predicting Deep Neural Network Generalization with Perturbation Response Curves
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of prediction tasks. However, despite these successes, the recent Predicting Gener…
Towards creativity characterization of generative models via group-based subset scanning
Celia Cintas, Payel Das, Brian Quanz +3
Deep generative models, such as Variational Autoencoders (VAEs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution…
Gi and Pal Scores: Deep Neural Network Generalization Statistics
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks. However, despite these successes…