Publications

Vulnerability of terrestrial vertebrate food webs to anthropogenic threats in Europe

Louise M. J. O'Connor

Francesca Cosentino

Michael B. J. Harfoot

Luigi Maiorano

Chiara Mancino

Laura J. Pollock

Wilfried Thuiller

2024-03-22

Global Change Biology (published)

doi.org

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold

Maziar Sanjabi

Pietro Astolfi

Adriana Romero Soriano

Kamalika Chaudhuri

Mike Rabbat

Chuan Guo

2024-03-21

ArXiv (preprint)

doi.org

arxiv.org

Language Models Can Reduce Asymmetry in Information Markets

Nasim Rahaman

Martin Weiss

Manuel Wüthrich

Yoshua Bengio

Erran L. Li

Chris Pal

Bernhard Schölkopf

2024-03-21

ArXiv (preprint)

doi.org

arxiv.org

Multi-Resolution Continuous Normalizing Flows

Vikram Voleti

Chris Finlay

Adam M. Oberman

Chris Pal

2024-03-21

Annals of Mathematics and Artificial Intelligence (published)

doi.org

openreview.net

Assistive sensory-motor perturbations influence learned neural representations

Pavithra Rajeswaran

Alexandre Payeur

Guillaume Lajoie

Amy L. Orsborn

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using B… (see more)rain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.

2024-03-20

bioRxiv (preprint)

doi.org

From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards

Khaoula Chehbouni

Megha Roshan

Emmanuel Ma

Futian Andrew Wei

Afaf Taïk

Jackie Ck Cheung

Golnoosh Farnadi

2024-03-20

ArXiv (preprint)

doi.org

arxiv.org

HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling

Daniel Duenias

Brennan Nichyporuk

Tal Arbel

Tammy Riklin-Raviv

The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Reco… (see more)rds (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.

2024-03-20

ArXiv (preprint)

doi.org

arxiv.org

Unravelling the neural dynamics of hypnotic susceptibility: Aperiodic neural activity as a central feature of hypnosis

Mathieu Landry

Jason da Silva Castanheira

Catherine Boisvert

Floriane Rousseaux

Jérôme Sackur

Amir Raz

Philippe Richebé

David Ogez

Pierre Rainville

Karim Jerbi

2024-03-20

bioRxiv (preprint)

doi.org

Solving Combinatorial Pricing Problems using Embedded Dynamic Programming Models

Quang Minh Bui

Margarida Carvalho

Jos'e Neto

The combinatorial pricing problem (CPP) is a bilevel problem in which the leader maximizes their revenue by imposing tolls on certain items … (see more)that they can control. Based on the tolls set by the leader, the follower selects a subset of items corresponding to an optimal solution of a combinatorial optimization problem. To accomplish the leader's goal, the tolls need to be sufficiently low to discourage the follower from choosing the items offered by the competitors. In this paper, we derive a single-level reformulation for the CPP by rewriting the follower's problem as a longest path problem using a dynamic programming model, and then taking its dual and applying strong duality. We proceed to solve the reformulation in a dynamic fashion with a cutting plane method. We apply this methodology to 2 distinct dynamic programming models, namely, a novel formulation designated as selection diagram and the well-known decision diagram. We also produce numerical results to evaluate their performances across 3 different specializations of the CPP and a closely related problem that is the knapsack interdiction problem. Our results showcase the potential of the 2 proposed reformulations over the natural value function approach, expanding the set of tools to solve combinatorial bilevel programs.

2024-03-19

ArXiv (preprint)

doi.org

arxiv.org

Two-stage Multiple-Model Compression Approach for Sampled Electrical Signals

Corentin Presvôts

Michel Kieffer

Thibault Prevost

Patrick Panciatici

Zuxing Li

Pablo Piantanida

This paper presents a two-stage Multiple-Model Compression (MMC) approach for sampled electrical waveforms. To limit latency, the processing… (see more) is window-based, with a window length commensurate to the electrical period. For each window, the first stage compares several parametric models to get a coarse representation of the samples. The second stage then compares different residual compression techniques to minimize the norm of the reconstruction error. The allocation of the rate budget among the two stages is optimized. The proposed MMC approach provides better signal-to-noise ratios than state-of-the-art solutions on periodic and transient waveforms.

2024-03-19

Data Compression Conference (published)

doi.org

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

Ali Karami

Thi Kieu Khanh Ho

Narges Armanfard

2024-03-18

ArXiv (preprint)

doi.org

arxiv.org

Offline Multitask Representation Learning for Reinforcement Learning