Payel Das

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 121

Top Venues

Publications

Georgios Kollias, Payel Das, Subhajit Chaudhury
Generation Constraint Scaling Can Mitigate Hallucination. CoRR (2024)
Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen
Larimar: Large Language Models with Episodic Memory Control. CoRR (2024)
Megh Thakkar, Quentin Fournier, Matthew D. Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, Sarath Chandar
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques. CoRR (2024)
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation. CoRR (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. CoRR (2024)
Jerret Ross, Brian Belgodere, Samuel C. Hoffman, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
GP-MoLFormer: A Foundation Model For Molecular Generation. CoRR (2024)
Megh Thakkar, Quentin Fournier, Matthew Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, Sarath Chandar
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques. ACL (1) (2024)
Hatem Helal, Jesun Firoz, Jenna A. Bilbrey, Henry Sprueill, Kristina M. Herman, Mario Michael Krell, Tom Murray, Manuel Lopez Roldan, Mike Kraus, Ang Li, Payel Das, Sotiris S. Xantheas, Sutanay Choudhury
Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units. J. Chem. Inf. Model. 64 (5) (2024)
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
Structure-Informed Protein Language Model. CoRR (2024)
Elliot Nelson, Georgios Kollias, Payel Das, Subhajit Chaudhury, Soham Dan
Needle in the Haystack for Memory Based Large Language Models. CoRR (2024)
Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. ACL (Findings) (2024)
Noelia Ferruz, Marinka Zitnik, Pierre-Yves Oudeyer, Emmie Hine, Nandana Sengupta, Yiyu Shi, Diana Mincu, Sebastian Porsdam Mann, Payel Das, Francesco Stella
Anniversary AI reflections. Nat. Mac. Intell. 6 (1) (2024)
Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, Mina Konakovic-Lukovic
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints. CoRR (2024)
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. AAAI (2023)
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
Reprogramming Pretrained Language Models for Antibody Sequence Infilling. ICML (2023)
Jack Scantlebury, Lucy Vost, Anna Carbery, Thomas E. Hadfield, Oliver M. Turnbull, Nathan Brown, Vijil Chenthamarakshan, Payel Das, Harold Grosjean, Frank von Delft, Charlotte M. Deane
A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening. J. Chem. Inf. Model. 63 (10) (2023)
Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson
Physics-enhanced deep surrogates for partial differential equations. Nat. Mac. Intell. 5 (12) (2023)
Zuobai Zhang, Minghao Xu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
Enhancing Protein Language Models with Structure-based Encoder and Pre-training. CoRR (2023)
Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Driggs Campbell, Payel Das, Lav R. Varshney
Efficient Equivariant Transfer Learning from Pretrained Models. NeurIPS (2023)
Ioana Baldini, Chhavi Yadav, Payel Das, Kush R. Varshney
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models. CoRR (2023)
Minghao Guo, Veronika Thost, Samuel W. Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction. CoRR (2023)
Minghao Guo, Veronika Thost, Samuel W. Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction. ICML (2023)
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, Siva Reddy
The Impact of Positional Encoding on Length Generalization in Transformers. NeurIPS (2023)
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, Siva Reddy
The Impact of Positional Encoding on Length Generalization in Transformers. CoRR (2023)
Pin-Yu Chen, Payel Das
AI Maintenance: A Robustness Perspective. Computer 56 (2) (2023)
Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Rose Driggs-Campbell, Payel Das, Lav R. Varshney
Equivariant Few-Shot Learning from Pretrained Models. CoRR (2023)
Pin-Yu Chen, Payel Das
AI Maintenance: A Robustness Perspective. CoRR (2023)
Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
Protein Representation Learning by Geometric Structure Pretraining. ICLR (2023)
Katy Ilonka Gero, Payel Das, Pierre L. Dognin, Inkit Padhi, Prasanna Sattigeri, Kush R. Varshney
The incentive gap in data work in the era of large models. Nat. Mac. Intell. 5 (6) (2023)
Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction. CoRR (2023)
Yonas Sium, Georgios Kollias, Tsuyoshi Idé, Payel Das, Naoki Abe, Aurélie C. Lozano, Qi Li
Direction Aware Positional and Structural Encoding for Directed Graph Neural Networks. ICASSP (2023)
Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction. NeurIPS (2023)
Ria Vinod, Pin-Yu Chen, Payel Das
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning. CoRR (2023)
Brian Belgodere, Vijil Chenthamarakshan, Payel Das, Pierre L. Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, Richard A. Young
Cloud-Based Real-Time Molecular Screening Platform with MolFormer. ECML/PKDD (6) (2022)
Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das
Optimizing molecules using efficient queries from property evaluations. Nat. Mach. Intell. 4 (1) (2022)
N. Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, Rongjie Lai
Learning Geometrically Disentangled Representations of Protein Folding Simulations. CoRR (2022)
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Active Sampling of Multiple Sources for Sequential Estimation. IEEE Trans. Signal Process. 70 (2022)
Vijil Chenthamarakshan, Samuel C. Hoffman, C. David Owen, Petra Lukacik, Claire Strain-Damerell, Daren Fearon, Tika R. Malla, Anthony Tumber, Christopher J. Schofield, Helen M. E. Duyvesteyn, Wanwisa Dejnirattisai, Loic Carrique, Thomas S. Walter, Gavin R. Screaton, Tetiana Matviiuk, Aleksandra Mojsilovic, Jason Crain, Martin A. Walsh, David I. Stuart, Payel Das
Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework. CoRR (2022)
Payel Das, Kapil Kant, B. V. Ratish Kumar
Modified Galerkin method for Volterra-Fredholm-Hammerstein integral equations. Comput. Appl. Math. 41 (6) (2022)
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. CoRR (2022)
Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
Biological Sequence Design with GFlowNets. ICML (2022)
Matteo Manica, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Jannis Born, Dean Clarke, Yves Gaetan Nana Teukam, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang-Han Hsu, Federico Zipoli, Oliver Schilter, Giorgio Giannone, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
GT4SD: Generative Toolkit for Scientific Discovery. CoRR (2022)
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling. CoRR (2022)
Igor Melnyk, Pierre L. Dognin, Payel Das
Knowledge Graph Generation From Text. EMNLP (Findings) (2022)
Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill, Sotiris S. Xantheas, Payel Das, Manuel Lopez Roldan, Mike Kraus, Hatem Helal, Sutanay Choudhury
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators. CoRR (2022)
Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das
Fourier Representations for Black-Box Optimization over Categorical Variables. AAAI (2022)
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik
Data-Efficient Graph Grammar Learning for Molecular Generation. ICLR (2022)
Igor Melnyk, Pierre L. Dognin, Payel Das
Knowledge Graph Generation From Text. CoRR (2022)
Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das
Fourier Representations for Black-Box Optimization over Categorical Variables. CoRR (2022)
Igor Melnyk, Aurélie C. Lozano, Payel Das, Vijil Chenthamarakshan
AlphaFold Distillation for Improved Inverse Protein Folding. CoRR (2022)
Brian Belgodere, Vijil Chenthamarakshan, Payel Das, Pierre L. Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, Richard A. Young
Cloud-Based Real-Time Molecular Screening Platform with MolFormer. CoRR (2022)
Chanakya Ekbote, Moksh Jain, Payel Das, Yoshua Bengio
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions. CoRR (2022)
Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning. CoRR (2022)
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das
Large-scale chemical language representations capture molecular structure and properties. Nat. Mac. Intell. 4 (12) (2022)
Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations. ICASSP (2022)
Payel Das, Lav R. Varshney
Explaining Artificial Intelligence Generation and Creativity: Human interpretability for novel ideas and artifacts. IEEE Signal Process. Mag. 39 (4) (2022)
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Active Sampling of Multiple Sources for Sequential Estimation. CoRR (2022)
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik
Data-Efficient Graph Grammar Learning for Molecular Generation. CoRR (2022)
Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
Biological Sequence Design with GFlowNets. CoRR (2022)
Zuobai Zhang, Minghao Xu, Arian R. Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
Protein Representation Learning by Geometric Structure Pretraining. CoRR (2022)
Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning. IJCAI (2022)
Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra, Payel Das, Luca Daniel
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data. CoRR (2022)
Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. CoRR (2022)
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Active Estimation From Multimodal Data. ICASSP (2021)
Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson
Physics-enhanced deep surrogates for PDEs. CoRR (2021)
Pierre L. Dognin, Inkit Padhi, Igor Melnyk, Payel Das
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models. CoRR (2021)
Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
Predicting Deep Neural Network Generalization with Perturbation Response Curves. CoRR (2021)
Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design. CoRR (2021)
Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
Predicting Deep Neural Network Generalization with Perturbation Response Curves. NeurIPS (2021)
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination. CoRR (2021)
Kahini Wadhawan, Payel Das, Barbara A. Han, Ilya R. Fischhoff, Adrian C. Castellanos, Arvind Varsani, Kush R. Varshney
Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention. CoRR (2021)
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Active Binary Classification of Random Fields. ISIT (2021)
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das
Do Large Scale Molecular Language Representations Capture Important Structural Information? CoRR (2021)
Celia Cintas, Payel Das, Brian Quanz, Skyler Speakman, Victor Akinwande, Pin-Yu Chen
Towards creativity characterization of generative models via group-based subset scanning. CoRR (2021)
Samuel C. Hoffman, Vijil Chenthamarakshan, Dmitry Yu. Zubarev, Daniel P. Sanders, Payel Das
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model. CoRR (2021)
Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurelie C. Lozano
Benchmarking deep generative models for diverse antibody sequence design. CoRR (2021)
Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das
Self-Progressing Robust Training. AAAI (2021)
Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations. CoRR (2021)
Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design. ICML (2021)
Pierre L. Dognin, Inkit Padhi, Igor Melnyk, Payel Das
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models. EMNLP (1) (2021)
Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
Gi and Pal Scores: Deep Neural Network Generalization Statistics. CoRR (2021)
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Best Arm Identification in Contaminated Stochastic Bandits. NeurIPS (2021)
Ria Vinod, Pin-Yu Chen, Payel Das
Reprogramming Language Models for Molecular Representation Learning. CoRR (2020)
Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David S. Kung, Michael Picheny
Improving Efficiency in Large-Scale Decentralized Distributed Training. ICASSP (2020)
Inkit Padhi, Pierre L. Dognin, Ke Bai, Cícero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
Learning Implicit Text Generation via Feature Matching. CoRR (2020)
Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah
Toward a neuro-inspired creative decoder. IJCAI (2020)
Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Cícero Nogueira dos Santos, Payel Das
DualTKB: A Dual Learning Bridge between Text and Knowledge Base. EMNLP (1) (2020)
Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness. ICLR (2020)
N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Optimizing Mode Connectivity via Neuron Alignment. CoRR (2020)
Kar Wai Lim, Bhanushee Sharma, Payel Das, Vijil Chenthamarakshan, Jonathan S. Dordick
Explaining Chemical Toxicity using Missing Features. CoRR (2020)
N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Optimizing Mode Connectivity via Neuron Alignment. NeurIPS (2020)
Inkit Padhi, Pierre L. Dognin, Ke Bai, Cícero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
Learning Implicit Text Generation via Feature Matching. ACL (2020)
Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness. CoRR (2020)
Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Cícero Nogueira dos Santos, Payel Das
DualTKB: A Dual Learning Bridge between Text and Knowledge Base. CoRR (2020)
Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. ICLR (2020)
Vijil Chenthamarakshan, Payel Das, Inkit Padhi, Hendrik Strobelt, Kar Wai Lim, Benjamin Hoover, Samuel C. Hoffman, Aleksandra Mojsilovic
Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. CoRR (2020)
Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. NeurIPS (2020)
Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. NeurIPS (2020)
Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das
Self-Progressing Robust Training. CoRR (2020)
Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das
Optimizing Molecules using Efficient Queries from Property Evaluations. CoRR (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Payel Das