Search Results for author: Inkit Padhi

Found 18 papers, 5 papers with code

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.

Frame Graph Generation +3

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

Alleviating Noisy Data in Image Captioning with Cooperative Distillation

no code implementations21 Dec 2020 Pierre Dognin, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff

Image captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images.

Image Captioning

Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge

1 code implementation21 Dec 2020 Pierre Dognin, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, Richard A. Young, Brian Belgodere

Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO.

Image Captioning

Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text

no code implementations8 Dec 2020 Nishtha Madaan, Inkit Padhi, Naveen Panwar, Diptikalyan Saha

Aligned with this, we propose a framework GYC, to generate a set of counterfactual text samples, which are crucial for testing these ML systems.

Data Augmentation TAG

Tabular Transformers for Modeling Multivariate Time Series

1 code implementation3 Nov 2020 Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre Dognin, Jerret Ross, Ravi Nair, Erik Altman

This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.

Fraud Detection Synthetic Data Generation +1

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).

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

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.

Sobolev Independence Criterion

1 code implementation NeurIPS 2019 Youssef Mroueh, Tom Sercu, Mattia Rigotti, Inkit Padhi, Cicero dos Santos

In the kernel version we show that SIC can be cast as a convex optimization problem by introducing auxiliary variables that play an important role in feature selection as they are normalized feature importance scores.

Feature Importance

Generative Feature Matching Networks

no code implementations ICLR 2019 Cicero Nogueira dos Santos, Inkit Padhi, Pierre Dognin, Youssef Mroueh

We propose a non-adversarial feature matching-based approach to train generative models.

Learning Implicit Generative Models by Matching Perceptual Features

no code implementations ICCV 2019 Cicero Nogueira dos Santos, Youssef Mroueh, Inkit Padhi, Pierre Dognin

Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution.

online learning Style Transfer +2

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

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.

Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer

no code implementations ACL 2018 Cicero Nogueira dos Santos, Igor Melnyk, Inkit Padhi

We introduce a new approach to tackle the problem of offensive language in online social media.

Style Transfer Text Style Transfer +1

Improved Neural Text Attribute Transfer with Non-parallel Data

no code implementations26 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.

Disentanglement Text Attribute Transfer

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