Publications

Leveraging ChatGPT to Democratize and Decolonize Global Surgery: Large Language Models for Small Healthcare Budgets
Fabio Botelho
Jean Marie Tshimula
Dan Poenaru
2023-09-10
World Journal of Surgery (published)
doi.org
Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study
Etienne Combrisson
Franck Di Rienzo
Anne-Lise Saive
Marcela Perrone-Bertolotti
Juan LP Soto
Philippe Kahane
Jean-Philippe Lachaux
Aymeric Guillot
Karim Jerbi
2023-09-10
bioRxiv (preprint)
doi.org
Neural Causal Structure Discovery from Interventions
Nan Rosemary Ke
Olexa Bilaniuk
Anirudh Goyal
Stefan Bauer
Hugo Larochelle
Bernhard Schölkopf
Michael Curtis Mozer
Chris Pal
Yoshua Bengio
Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (see more) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.
2023-09-10
TMLR (accepted)
openreview.net
openreview.net
Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs
Tzung-Han Juang
Christof Schlaak
Christophe Dubach
2023-09-09
ACM Transactions on Embedded Computing Systems (published)
doi.org
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning
Zhizun Wang
David Meger
In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentang… (see more)led World Model to address the challenge of achieving a common goal of multiple agents interacting in the same environment with reduced sample complexity. Due to scalability and non-stationarity problems posed by multi-agent systems, model-free methods rely on a considerable number of samples for training. In contrast, we use a modularized world model, composed of action-conditioned, action-free, and static branches, to unravel the environment dynamics and produce imagined outcomes based on past experience, without sampling directly from the real environment. We employ variational auto-encoders and variational graph auto-encoders to learn the latent representations for the world model, which is merged with a value-based framework to predict the joint action-value function and optimize the overall training objective. We present experimental results in Easy, Hard, and Super-Hard StarCraft II micro-management challenges to demonstrate that our method achieves high sample efficiency and exhibits superior performance in defeating the enemy armies compared to other baselines.
2023-09-08
ArXiv (preprint)
doi.org
arxiv.org
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piché
Valentin Thomas
Joseph Marino
Gian Maria Marconi
Rafael Pardinas
Chris Pal
Mohammad Emtiyaz Khan
2023-09-06
TMLR (accepted)
doi.org
openreview.net
Cardiomyocyte orientation recovery at micrometer scale reveals long‐axis fiber continuum in heart walls
Drisya Dileep
Tabish A Syed
Tyler FW Sloan
Perundurai S Dhandapany
Kaleem Siddiqi
Minhajuddin Sirajuddin
2023-09-06
The EMBO Journal (published)
doi.org
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy
Sékou-Oumar Kaba
Carmelo Gonzales
Santiago Miret
Siamak Ravanbakhsh
2023-09-06
ArXiv (preprint)
doi.org
arxiv.org
Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface
Atieh Taheri
Mohammad Izadi
Gururaj Shriram
Negar Rostamzadeh
Shaun Kane
Text-to-image generation models have grown in popularity due to their ability to produce high-quality images from a text prompt. One use for… (see more) this technology is to enable the creation of more accessible art creation software. In this paper, we document the development of an alternative user interface that reduces the typing effort needed to enter image prompts by providing suggestions from a large language model, developed through iterative design and testing within the project team. The results of this testing demonstrate how generative text models can support the accessibility of text-to-image models, enabling users with a range of abilities to create visual art.
2023-09-05
ArXiv (preprint)
doi.org
arxiv.org
Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures
Option Pricing
Saeed Marzban
Érick Delage
Jonathan Yu-Meng Li
2023-09-05
Quantitative Finance (published)
doi.org
Tidying Up the Conversational Recommender Systems' Biases
Armin Moradi
Golnoosh Farnadi
The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research… (see more) circles. However, concerns regarding biases in these systems have emerged. While individual components of CRS have been subject to bias studies, a literature gap remains in understanding specific biases unique to CRS and how these biases may be amplified or reduced when integrated into complex CRS models. In this paper, we provide a concise review of biases in CRS by surveying recent literature. We examine the presence of biases throughout the system's pipeline and consider the challenges that arise from combining multiple models. Our study investigates biases in classic recommender systems and their relevance to CRS. Moreover, we address specific biases in CRS, considering variations with and without natural language understanding capabilities, along with biases related to dialogue systems and language models. Through our findings, we highlight the necessity of adopting a holistic perspective when dealing with biases in complex CRS models.
2023-09-05
ArXiv (preprint)
doi.org
arxiv.org
xSA: A Binary Cross-Entropy Simulated Annealing Polar Decoder
Ryan Seah
Huayi Zhou
Marwan Jalaleddine
Warren Gross
Polar decoders such as successive-cancellation and successive-cancellation list decoders are limited by their sequential nature, which leads… (see more) to a linear increase in latency with the codeword length. Heuristic based decoders such as quantum annealing have been proposed to overcome this limitation. However, these decoders have shown poor performance when decoding polar codes with more than eight bits. In this paper, we developed new meta-heuristic based polar decoder, called xSA, which uses a new receiver constraint modeled by the binary cross-entropy function. We also propose a method to determine the weights used in a quadratic unconstrained binary optimization (QUBO) function. The polar code is assumed to have been sent across an AWGN channel and we conducted our experiments and simulations using PyQUBO and dwave-neal. Our results show that xSA is able to decode codes of length 16 and 32 with a near-ML FER performance, presenting itself as a promising alternative to traditional polar decoders for real world applications and next generation cellular communications.
2023-09-04
International Symposium on Turbo Codes and Iterative Information Processing (published)
doi.org