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2020 – today
- 2024
[j23]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): 1568-1580 (2024)- [j22]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): 6-12 (2024) - [c41]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: 2416-2430 - [c40]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: 5732-5745 - [c39]Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarathkrishna 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. ICML 2024 - [c38]Ching-Yun Ko, Pin-Yu Chen, Payel Das, Jeet Mohapatra, Luca Daniel:
What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks. ICML 2024 - [c37]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. ICML 2024 - [i67]Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Structure-Informed Protein Language Model. CoRR abs/2402.05856 (2024) - [i66]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 abs/2402.07692 (2024) - [i65]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 abs/2402.07955 (2024) - [i64]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 abs/2403.11901 (2024) - [i63]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 abs/2404.01306 (2024) - [i62]Jerret Ross, Brian Belgodere, Samuel C. Hoffman, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das:
GP-MoLFormer: A Foundation Model For Molecular Generation. CoRR abs/2405.04912 (2024) - [i61]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 abs/2406.04879 (2024) - [i60]Elliot Nelson, Georgios Kollias, Payel Das, Subhajit Chaudhury, Soham Dan:
Needle in the Haystack for Memory Based Large Language Models. CoRR abs/2407.01437 (2024) - [i59]Georgios Kollias, Payel Das, Subhajit Chaudhury:
Generation Constraint Scaling Can Mitigate Hallucination. CoRR abs/2407.16908 (2024) - 2023
- [j21]Pin-Yu Chen, Payel Das:
AI Maintenance: A Robustness Perspective. Computer 56(2): 48-56 (2023) - [j20]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): 2960-2974 (2023) - [j19]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): 565-567 (2023) - [j18]Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson:
Physics-enhanced deep surrogates for partial differential equations. Nat. Mac. Intell. 5(12): 1458-1465 (2023) - [c36]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: 6788-6796 - [c35]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: 1-5 - [c34]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 - [c33]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: 12055-12076 - [c32]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: 24398-24419 - [c31]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 - [c30]Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, Siva Reddy:
The Impact of Positional Encoding on Length Generalization in Transformers. NeurIPS 2023 - [c29]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 - [i58]Ria Vinod, Pin-Yu Chen, Payel Das:
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning. CoRR abs/2301.02120 (2023) - [i57]Pin-Yu Chen, Payel Das:
AI Maintenance: A Robustness Perspective. CoRR abs/2301.03052 (2023) - [i56]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 abs/2301.12068 (2023) - [i55]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 abs/2303.06275 (2023) - [i54]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 abs/2305.09900 (2023) - [i53]Ioana Baldini, Chhavi Yadav, Payel Das, Kush R. Varshney:
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models. CoRR abs/2305.12620 (2023) - [i52]Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, Siva Reddy:
The Impact of Positional Encoding on Length Generalization in Transformers. CoRR abs/2305.19466 (2023) - [i51]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 abs/2309.01788 (2023) - 2022
- [j17]Payel Das, Kapil Kant, B. V. Ratish Kumar:
Modified Galerkin method for Volterra-Fredholm-Hammerstein integral equations. Comput. Appl. Math. 41(6) (2022) - [j16]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): 21-31 (2022) - [j15]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): 1256-1264 (2022) - [j14]Payel Das, Lav R. Varshney:
Explaining Artificial Intelligence Generation and Creativity: Human interpretability for novel ideas and artifacts. IEEE Signal Process. Mag. 39(4): 85-95 (2022) - [j13]Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das:
Active Sampling of Multiple Sources for Sequential Estimation. IEEE Trans. Signal Process. 70: 4571-4585 (2022) - [c28]Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das:
Fourier Representations for Black-Box Optimization over Categorical Variables. AAAI 2022: 10156-10165 - [c27]Igor Melnyk, Pierre L. Dognin, Payel Das:
Knowledge Graph Generation From Text. EMNLP (Findings) 2022: 1610-1622 - [c26]Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das:
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations. ICASSP 2022: 3783-3787 - [c25]Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik:
Data-Efficient Graph Grammar Learning for Molecular Generation. ICLR 2022 - [c24]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: 9786-9801 - [c23]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: 4929-4935 - [c22]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: 641-644 - [i50]Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das:
Fourier Representations for Black-Box Optimization over Categorical Variables. CoRR abs/2202.03712 (2022) - [i49]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 abs/2203.00523 (2022) - [i48]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 abs/2203.04115 (2022) - [i47]Zuobai Zhang, Minghao Xu, Arian R. Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Protein Representation Learning by Geometric Structure Pretraining. CoRR abs/2203.06125 (2022) - [i46]Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik:
Data-Efficient Graph Grammar Learning for Molecular Generation. CoRR abs/2203.08031 (2022) - [i45]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 abs/2204.09042 (2022) - [i44]N. Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, Rongjie Lai:
Learning Geometrically Disentangled Representations of Protein Folding Simulations. CoRR abs/2205.10423 (2022) - [i43]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 abs/2207.03928 (2022) - [i42]Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam:
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. CoRR abs/2207.07174 (2022) - [i41]Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das:
Active Sampling of Multiple Sources for Sequential Estimation. CoRR abs/2208.05406 (2022) - [i40]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 abs/2208.06665 (2022) - [i39]Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra, Payel Das, Luca Daniel:
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data. CoRR abs/2210.02989 (2022) - [i38]Igor Melnyk, Aurélie C. Lozano, Payel Das, Vijil Chenthamarakshan:
AlphaFold Distillation for Improved Inverse Protein Folding. CoRR abs/2210.03488 (2022) - [i37]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 abs/2210.06475 (2022) - [i36]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 abs/2210.07144 (2022) - [i35]Chanakya Ekbote, Moksh Jain, Payel Das, Yoshua Bengio:
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions. CoRR abs/2211.00568 (2022) - [i34]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 abs/2211.04598 (2022) - [i33]Igor Melnyk, Pierre L. Dognin, Payel Das:
Knowledge Graph Generation From Text. CoRR abs/2211.10511 (2022) - 2021
- [c21]Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das:
Self-Progressing Robust Training. AAAI 2021: 7107-7115 - [c20]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: 1084-1099 - [c19]Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das:
Active Estimation From Multimodal Data. ICASSP 2021: 5180-5184 - [c18]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: 1261-1271 - [c17]Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das:
Active Binary Classification of Random Fields. ISIT 2021: 3326-3331 - [c16]Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das:
Best Arm Identification in Contaminated Stochastic Bandits. NeurIPS 2021: 9651-9662 - [c15]Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen:
Predicting Deep Neural Network Generalization with Perturbation Response Curves. NeurIPS 2021: 21176-21188 - [i32]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 abs/2104.00479 (2021) - [i31]Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen:
Gi and Pal Scores: Deep Neural Network Generalization Statistics. CoRR abs/2104.03469 (2021) - [i30]Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das:
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations. CoRR abs/2106.04464 (2021) - [i29]Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen:
Predicting Deep Neural Network Generalization with Perturbation Response Curves. CoRR abs/2106.04765 (2021) - [i28]Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das:
Do Large Scale Molecular Language Representations Capture Important Structural Information? CoRR abs/2106.09553 (2021) - [i27]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 abs/2106.13058 (2021) - [i26]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 abs/2108.08077 (2021) - [i25]Pierre L. Dognin, Inkit Padhi, Igor Melnyk, Payel Das:
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models. CoRR abs/2108.12472 (2021) - [i24]Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson:
Physics-enhanced deep surrogates for PDEs. CoRR abs/2111.05841 (2021) - [i23]Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurélie C. Lozano:
Benchmarking deep generative models for diverse antibody sequence design. CoRR abs/2111.06801 (2021) - [i22]Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das:
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination. CoRR abs/2111.07458 (2021) - [i21]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 abs/2112.01625 (2021) - 2020
- [c14]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: 3855-3863 - [c13]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: 8605-8616 - [c12]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: 3022-3026 - [c11]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 - [c10]Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin:
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness. ICLR 2020 - [c9]Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah:
Toward a neuro-inspired creative decoder. IJCAI 2020: 2746-2753 - [c8]Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan:
Combinatorial Black-Box Optimization with Expert Advice. KDD 2020: 1918-1927 - [c7]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 - [c6]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 - [c5]N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai:
Optimizing Mode Connectivity via Neuron Alignment. NeurIPS 2020 - [i20]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. CoRR abs/2002.01119 (2020) - [i19]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 abs/2004.01215 (2020) - [i18]Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin:
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness. CoRR abs/2005.00060 (2020) - [i17]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 abs/2005.03588 (2020) - [i16]Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu S. Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cícero Nogueira dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy Tan, James Hedrick, Jason Crain, Aleksandra Mojsilovic:
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics. CoRR abs/2005.11248 (2020) - [i15]Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios, Payel Das, Samuel C. Hoffman, Troy David Loeffler, Subramanian Sankaranarayanan:
Combinatorial Black-Box Optimization with Expert Advice. CoRR abs/2006.03963 (2020) - [i14]Raphaël Pestourie, Youssef Mroueh, Thanh V. Nguyen, Payel Das, Steven G. Johnson:
Active learning of deep surrogates for PDEs: Application to metasurface design. CoRR abs/2008.12649 (2020) - [i13]N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai:
Optimizing Mode Connectivity via Neuron Alignment. CoRR abs/2009.02439 (2020) - [i12]Kar Wai Lim, Bhanushee Sharma, Payel Das, Vijil Chenthamarakshan, Jonathan S. Dordick:
Explaining Chemical Toxicity using Missing Features. CoRR abs/2009.12199 (2020) - [i11]Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy, Payel Das:
Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics. CoRR abs/2010.08548 (2020) - [i10]Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Cícero Nogueira dos Santos, Payel Das:
DualTKB: A Dual Learning Bridge between Text and Knowledge Base. CoRR abs/2010.14660 (2020) - [i9]Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das:
Optimizing Molecules using Efficient Queries from Property Evaluations. CoRR abs/2011.01921 (2020) - [i8]Ria Vinod, Pin-Yu Chen, Payel Das:
Reprogramming Language Models for Molecular Representation Learning. CoRR abs/2012.03460 (2020) - [i7]Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das:
Self-Progressing Robust Training. CoRR abs/2012.11769 (2020)
2010 – 2019
- 2019
- [j12]Nilofar Nahid, Payel Das, Gnaneshwar Nelakanti:
Projection and multi projection methods for nonlinear integral equations on the half-line. J. Comput. Appl. Math. 359: 119-144 (2019) - [c4]Payel Das, Nilofar Nahid, Gnaneshwar Nelakanti:
Superconvergence of Iterated Galerkin Method for a Class of Nonlinear Fredholm Integral Equations. ICITAM 2019: 53-74 - [c3]Tom Sercu, Sebastian Gehrmann, Hendrik Strobelt, Payel Das, Inkit Padhi, Cícero Nogueira dos Santos, Kahini Wadhawan, Vijil Chenthamarakshan:
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection. DGS@ICLR 2019 - [i6]Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn:
Toward A Neuro-inspired Creative Decoder. CoRR abs/1902.02399 (2019) - [i5]Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das:
Decentralized Parallel Algorithm for Training Generative Adversarial Nets. CoRR abs/1910.12999 (2019) - [i4]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. CoRR abs/1912.11940 (2019) - 2018
- [j11]Payel Das, Gnaneshwar Nelakanti, Guangqing Long:
Discrete Legendre spectral Galerkin method for Urysohn integral equations. Int. J. Comput. Math. 95(3): 465-489 (2018) - [c2]Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das:
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. NeurIPS 2018: 590-601 - [i3]Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das:
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. CoRR abs/1802.07623 (2018) - [i2]Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cícero Nogueira dos Santos, Matthew Riemer, Inkit Padhi, Vijil Chenthamarakshan, Aleksandra Mojsilovic:
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences. CoRR abs/1810.07743 (2018) - 2017
- [c1]Hongyuan You, Adam Liska, Nathan Russell, Payel Das:
Automated brain state identification using graph embedding. PRNI 2017: 1-5 - [i1]Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels:
Neurology-as-a-Service for the Developing World. CoRR abs/1711.06195 (2017) - 2016
- [j10]Payel Das, Gnaneshwar Nelakanti:
Corrigendum to: "Convergence analysis of discrete legendre spectral projection methods for hammerstein integral equations of mixed type" Applied Mathematics and Computation Volume 265, 15 August 2015, Pages 574-601. Appl. Math. Comput. 281: 394-395 (2016) - [j9]Payel Das, Gnaneshwar Nelakanti, Guangqing Long:
Erratum to: Discrete Legendre spectral projection methods for Fredholm-Hammerstein integral equations [J. Comput. Appl. Math 278 (2015) 293-305]. J. Comput. Appl. Math. 292: 634-636 (2016) - [j8]Payel Das, Mitali Madhumita Sahani, Gnaneshwar Nelakanti, Guangqing Long:
Legendre Spectral Projection Methods for Fredholm-Hammerstein Integral Equations. J. Sci. Comput. 68(1): 213-230 (2016) - 2015
- [j7]Payel Das, Gnaneshwar Nelakanti:
Convergence analysis of discrete legendre spectral projection methods for hammerstein integral equations of mixed type. Appl. Math. Comput. 265: 574-601 (2015) - [j6]Payel Das, Gnaneshwar Nelakanti, Guangqing Long:
Discrete Legendre spectral projection methods for Fredholm-Hammerstein integral equations. J. Comput. Appl. Math. 278: 293-305 (2015) - 2014
- [j5]Payel Das, Mitali Madhumita Sahani, Gnaneshwar Nelakanti:
Legendre spectral projection methods for Urysohn integral equations. J. Comput. Appl. Math. 263: 88-102 (2014) - [j4]Liesje Van Gelder, Payel Das, Hans Janssen, Staf Roels:
Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners. Simul. Model. Pract. Theory 49: 245-257 (2014) - 2011
- [j3]Zhen Xia, Payel Das, Tien Huynh, Ajay K. Royyuru, Ruhong Zhou:
Modeling mutations of influenza virus with IBM Blue Gene. IBM J. Res. Dev. 55(5): 7 (2011)
2000 – 2009
- 2009
- [j2]Payel Das, Jingyuan Li, Ajay K. Royyuru, Ruhong Zhou:
Free energy simulations reveal a double mutant avian H5N1 virus hemagglutinin with altered receptor binding specificity. J. Comput. Chem. 30(11): 1654-1663 (2009) - 2006
- [j1]Payel Das, Mark Moll, Hernán Stamati, Lydia E. Kavraki, Cecilia Clementi:
Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction. Proc. Natl. Acad. Sci. USA 103(26): 9885-9890 (2006)
Coauthor Index
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