429 citations · 665 across the 28 of their papers we have counts for
8 papers · 1 filter
Alleviating Noisy Data in Image Captioning with Cooperative Distillation
Pierre Dognin, Igor Melnyk, Youssef Mroueh +4
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…
Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
Pierre Dognin, Igor Melnyk, Youssef Mroueh +6
Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO. Often work in…
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Nishtha Madaan, Inkit Padhi, Naveen Panwar +1
Machine Learning has seen tremendous growth recently, which has led to larger adoption of ML systems for educational assessments, credit risk, healthcare, employment, criminal just…
Tabular Transformers for Modeling Multivariate Time Series
Inkit Padhi, Yair Schiff, Igor Melnyk +6
Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock t…
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
Pierre L. Dognin, Igor Melnyk, Inkit Padhi +2
In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak s…
Learning Implicit Text Generation via Feature Matching
Inkit Padhi, Pierre Dognin, Ke Bai +4
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 netwo…
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
Payel Das, Tom Sercu, Kahini Wadhawan +12
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled La…
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman +8
The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Ge…