activity
20152023
most citedRobust Motion In-betweening

259 citations · 976 across the 39 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.LG2023

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

Julien Roy, Pierre-Luc Bacon, Christopher Pal +1

In recent years, in-silico molecular design has received much attention from the machine learning community. When designing a new compound for pharmaceutical applications, there ar…

cs.CL20232 cited

Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans

Stefania Raimondo, Christopher Pal, Xiaotian Liu +2

Task-oriented dialogue is difficult in part because it involves understanding user intent, collecting information from the user, executing API calls, and generating helpful and flu…

cs.CL2023

Block-State Transformers

Mahan Fathi, Jonathan Pilault, Orhan Firat +3

State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies and efficiently scale to long sequences owing to their subquadratic r…

cs.CV20232 cited

ArK: Augmented Reality with Knowledge Interactive Emergent Ability

Qiuyuan Huang, Jae Sung Park, Abhinav Gupta +8

Despite the growing adoption of mixed reality and interactive AI agents, it remains challenging for these systems to generate high quality 2D/3D scenes in unseen environments. The…

stat.ML2023

Conservative objective models are a special kind of contrastive divergence-based energy model

Christopher Beckham, Christopher Pal

In this work we theoretically show that conservative objective models (COMs) for offline model-based optimisation (MBO) are a special kind of contrastive divergence-based energy mo…

cs.CL2023

Language Decision Transformers with Exponential Tilt for Interactive Text Environments

Nicolas Gontier, Pau Rodriguez, Issam Laradji +2

Text-based game environments are challenging because agents must deal with long sequences of text, execute compositional actions using text and learn from sparse rewards. We addres…