Search Results for author: Payel Das

Found 41 papers, 8 papers with code

Best Arm Identification in Contaminated Stochastic Bandits

no code implementations NeurIPS 2021 Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das

Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.

Benchmarking deep generative models for diverse antibody sequence design

no code implementations12 Nov 2021 Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurelie Lozano

Here we consider three recently proposed deep generative frameworks for protein design: (AR) the sequence-based autoregressive generative model, (GVP) the precise structure-based graph neural network, and Fold2Seq that leverages a fuzzy and scale-free representation of a three-dimensional fold, while enforcing structure-to-sequence (and vice versa) consistency.

Physics-enhanced deep surrogates for PDEs

no code implementations10 Nov 2021 Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson

We present a "physics-enhanced deep-surrogate ("PEDS") approach towards developing fast surrogate models for complex physical systems described by partial differential equations (PDEs) and similar models: we show how to combine a low-fidelity "coarse" solver with a neural network that generates "coarsified'' inputs, trained end-to-end to globally match the output of an expensive high-fidelity numerical solver.

Active Learning

ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models

1 code implementation EMNLP 2021 Pierre L. Dognin, Inkit Padhi, Igor Melnyk, Payel Das

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.

Graph Generation Text Generation

Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention

no code implementations18 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.

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

1 code implementation24 Jun 2021 Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang shen

Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.

Do Large Scale Molecular Language Representations Capture Important Structural Information?

no code implementations17 Jun 2021 Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das

Various representation learning methods in a supervised setting, including the features extracted using graph neural nets, have emerged for such tasks.

Drug Discovery Molecular Property Prediction +1

Predicting Deep Neural Network Generalization with Perturbation Response Curves

no code implementations NeurIPS 2021 Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization.

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations

no code implementations8 Jun 2021 Yair Schiff, Vijil Chenthamarakshan, Samuel Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das

Deep generative models have emerged as a powerful tool for learning informative molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design.

Drug Discovery Topological Data Analysis

Gi and Pal Scores: Deep Neural Network Generalization Statistics

no code implementations8 Apr 2021 Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks.

Towards creativity characterization of generative models via group-based subset scanning

no code implementations1 Apr 2021 Celia Cintas, Payel Das, Brian Quanz, Skyler Speakman, Victor Akinwande, Pin-Yu Chen

We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models.

ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics

no code implementations1 Jan 2021 Norman Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, Rongjie Lai

Empowered by the disentangled latent space learning, the extrinsic latent embedding is successfully used for classification or property prediction of different drugs bound to a specific protein.

Fourier Representations for Black-Box Optimization over Categorical Variables

no code implementations1 Jan 2021 Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam, Payel Das

In order to improve the performance and sample efficiency of such acquisition methods, we propose to use existing acquisition methods in conjunction with a surrogate model for the black-box evaluations over purely categorical variables.

Self-Progressing Robust Training

1 code implementation22 Dec 2020 Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems.

Adversarial Robustness

Reprogramming Language Models for Molecular Representation Learning

no code implementations7 Dec 2020 Ria Vinod, Pin-Yu Chen, Payel Das

Recent advancements in transfer learning have made it a promising approach for domain adaptation via transfer of learned representations.

Dictionary Learning Domain Adaptation +2

Optimizing Molecules using Efficient Queries from Property Evaluations

1 code implementation3 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.

DualTKB: A Dual Learning Bridge between Text and Knowledge Base

no code implementations EMNLP 2020 Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos santos, Payel Das

In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs).

Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics

no code implementations NeurIPS Workshop TDA_and_Beyond 2020 Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy, Payel Das

In this work, we propose a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features of molecular datasets by correlating latent space metrics with metrics from the field of topological data analysis (TDA).

Topological Data Analysis

Explaining Chemical Toxicity using Missing Features

no code implementations23 Sep 2020 Kar Wai Lim, Bhanushee Sharma, Payel Das, Vijil Chenthamarakshan, Jonathan S. Dordick

Chemical toxicity prediction using machine learning is important in drug development to reduce repeated animal and human testing, thus saving cost and time.

Optimizing Mode Connectivity via Neuron Alignment

1 code implementation NeurIPS 2020 N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai

Yet, current curve finding algorithms do not consider the influence of symmetry in the loss surface created by model weight permutations.

Active learning of deep surrogates for PDEs: Application to metasurface design

no code implementations24 Aug 2020 Raphaël Pestourie, Youssef Mroueh, Thanh V. Nguyen, Payel Das, Steven G. Johnson

Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components.

Active Learning

Learning Implicit Text Generation via Feature Matching

no code implementations ACL 2020 Inkit Padhi, Pierre Dognin, Ke Bai, Cicero Nogueira dos santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das

Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.

Conditional Text Generation Style Transfer +2

Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

2 code implementations ICLR 2020 Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin

In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness.

Adversarial Robustness

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

no code implementations NeurIPS 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 also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.

Improving Efficiency in Large-Scale Decentralized Distributed Training

no code implementations4 Feb 2020 Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David Kung, Michael Picheny

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks.

Speech Recognition

Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets

no code implementations ICLR 2020 Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei zhang, Xiaodong Cui, Payel Das, Tianbao Yang

Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $O\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets

no code implementations NeurIPS 2020 Mingrui Liu, Wei zhang, Youssef Mroueh, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das

Despite recent progress on decentralized algorithms for training deep neural networks, it remains unclear whether it is possible to train GANs in a decentralized manner.

Optimizing Loss Landscape Connectivity via Neuron Alignment

no code implementations25 Sep 2019 N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai

Empirically, this initialization is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes.

SPROUT: Self-Progressing Robust Training

no code implementations25 Sep 2019 Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy and reliable machine learning systems.

Adversarial Robustness

Surrogate-Based Constrained Langevin Sampling With Applications to Optimal Material Configuration Design

no code implementations25 Sep 2019 Thanh V Nguyen, Youssef Mroueh, Samuel C. Hoffman, Payel Das, Pierre Dognin, Giuseppe Romano, Chinmay Hegde

We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.

Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection

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.

Model Selection

Toward A Neuro-inspired Creative Decoder

no code implementations6 Feb 2019 Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah

Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain.

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

no code implementations17 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.

Autism Classification Using Brain Functional Connectivity Dynamics and Machine Learning

no code implementations21 Dec 2017 Ravi Tejwani, Adam Liska, Hongyuan You, Jenna Reinen, Payel Das

The goal of the present study is to identify autism using machine learning techniques and resting-state brain imaging data, leveraging the temporal variability of the functional connections (FC) as the only information.

General Classification

Neurology-as-a-Service for the Developing World

no code implementations16 Nov 2017 Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels

In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation.

EEG Feature Engineering

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