Publications

Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct

Peter Henderson

Jieru Hu

Mona Diab

Joelle Pineau

2024-03-11

Proceedings of the Symposium on Computer Science and Law (publié)

doi.org

Continuous iron spreading on carbon-shell composite nanotubes for electromagnetic wave absorption

Yuanyuan Zhang

Yining Li

Can Zhang

Zhenjie Guan

Liang Zhen

Jian-Tang Jiang

2024-03-10

Communications Materials (publié)

doi.org

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport

Nikolay Malkin

Yanlei Zhang

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their si… (voir plus)mulation-based maximum likelihood training. We introduce the generalized \textit{conditional flow matching} (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schrödinger bridge inference.

2024-03-10

TMLR (accepté)

openreview.net

IntentGPT: Few-shot Intent Discovery with Large Language Models

Juan A. Rodriguez

Nicholas Botzer

David Vázquez

Christopher Pal

Marco Pedersoli

Issam Hadj Laradji

2024-03-10

ICLR.cc/2024/Workshop/LLMAgents (poster)

doi.org

openreview.net

Investigating Robot Influence on Human Behaviour By Leveraging Entrainment Effects

Lixiao Zhu

AJung Moon

2024-03-10

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (publié)

doi.org

Long-term survival and functional outcomes of critically ill patients with hematologic malignancies: a Canadian multicenter prospective study

Laveena Munshi

Guillaume Dumas

Bram Rochwerg

Farah Shoukat

Michael Detsky

Dean A. Fergusson

Bruno Ferreyro

Paul Heffernan

Margaret Herridge

Sheldon Magder

Mark Minden

Rakesh Patel

Salman Qureshi

Aaron Schimmer

Santhosh Thyagu

Han Ting Wang

Sangeeta Mehta

2024-03-10

Intensive Care Medicine (publié)

doi.org

Perspectives on Robotic Systems for the Visually Impaired.

Joseph Paul Nemargut

Many roboticists hope to build robots and develop technologies that would one day help vulnerable populations to improve their quality of li… (voir plus)fe. As there are over 2.2 billion people with visual impairments in the world, this vulnerable population is a prime target for robotic assistants to help. In a discussion with a Certified Orientation and Mobility Specialist, someone who helps individuals with visual impairments navigate and perform daily tasks effectively, some interesting and counterintuitive questions were raised about technological developments, particularly robots. While these devices were meant to help the BVI population, many are, in reality, not practically beneficial. In this article, we highlight certain misconceptions about the BVI population and their needs. We emphasize the mismatch between robotics research and the needs of the individuals with visual impairments, especially from the lens of HRI researchers.

2024-03-10

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (publié)

doi.org

Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science

Xiangru Tang

Qiao Jin

Kunlun Zhu

Tongxin Yuan

Yichi Zhang

Wangchunshu Zhou

Meng Qu

Yilun Zhao

Jian Tang

Zhuosheng Zhang

Arman Cohan

Zhiyong Lu

Mark Gerstein

2024-03-10

ICLR.cc/2024/Workshop/LLMAgents (poster)

openreview.net

Generative Models for Decision Making

Bogdan Mazoure

Lisa Lee

Roberta Raileanu

Yilun Du

Walter Talbott

Katherine Metcalf

R Devon Hjelm

Alexander T Toshev

Generative Artificial Intelligence (AI) has made significant advancements in recent years, particularly with the development of large langua… (voir plus)ge and diffusion models. These generative models have demonstrated impressive capabilities in various tasks, such as text generation and image and audio synthesis. Concurrently, Reinforcement Learning (RL) has made significant strides in solving complex sequential decision-making problems with the help of external knowledge sources . However, there remains untapped potential in combining generative models with RL algorithms to tackle real-world challenges, particularly to improve sample efficiency of tabula rasa training by introducing priors from related domains such as visual question-answering, image captioning and image generation. This workshop aims to bring together researchers and practitioners from the fields of generative AI and reinforcement learning to explore the latest advances, methodologies, and applications. By fostering collaborations between these two domains, we intend to unlock new opportunities for addressing complex problems that lie at the intersection of both fields.

2024-03-07

ICLR.cc/2024/Workshop_Proposals (publié)

openreview.net