32 citations · 69 across the 10 of their papers we have counts for
11 papers
Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training
Youssef Mroueh, Nicolas Dupuis, Brian Belgodere +6
We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes. Our motivation comes from recent work on off-policy Proximal Policy Opti…
Distributional Preference Alignment of LLMs via Optimal Transport
Igor Melnyk, Youssef Mroueh, Brian Belgodere +6
Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…
GP-MoLFormer: A Foundation Model For Molecular Generation
Jerret Ross, Brian Belgodere, Samuel C. Hoffman +4
Transformer-based models trained on large and general purpose datasets consisting of molecular strings have recently emerged as a powerful tool for successfully modeling various st…
Risk Aware Benchmarking of Large Language Models
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti +6
We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical…
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
Brian Belgodere, Pierre Dognin, Adam Ivankay +11
Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate…
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
Brian Belgodere, Vijil Chenthamarakshan, Payel Das +9
With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based rea…
G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning
John R. Kender, Bishwaranjan Bhattacharjee, Parijat Dube +1
Transfer learning is a deep-learning technique that ameliorates the problem of learning when human-annotated labels are expensive and limited. In place of such labels, it uses inst…
Large-Scale Chemical Language Representations Capture Molecular Structure and Properties
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan +3
Models based on machine learning can enable accurate and fast molecular property predictions, which is of interest in drug discovery and material design. Various supervised machine…
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…
P2L: Predicting Transfer Learning for Images and Semantic Relations
Bishwaranjan Bhattacharjee, John R. Kender, Matthew Hill +7
Transfer learning enhances learning across tasks, by leveraging previously learned representations -- if they are properly chosen. We describe an efficient method to accurately est…