Publications

A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories

Ivan Grega

Félix Therrien

Abhishek Soni

Karry Ocean

Kevan Dettelbach

Ribwar Ahmadi

Mehrdad Mokhtari

Curtis P. Berlinguette

Yoshua Bengio

The electrochemical reduction of atmospheric CO…

2025-03-02

ICLR.cc/2025/Workshop/AI4MAT (poster)

doi.org

openreview.net

Platform-based Adaptive Experimental Research in Education: Lessons Learned from The Digital Learning Challenge

Ilya Musabirov

Mohi Reza

Haochen Song

Steven Moore

Pan Chen

Harsh Kumar

Tong Li

John Stamper

Norman Bier

Anna Rafferty

Thomas Price

Nina Deliu

Audrey Durand

Michael Liut

Joseph Jay Williams

: We report on our experience with a real-world, multi-experimental evaluation of an adaptive experimentation platform within the XPRIZE Dig… (voir plus)ital Learning Challenge framework. We showcase how EASI (Experiment as a Service) cross-platform software supports quick integration and deployment of adaptive experiments as well as five systematic replications within a 30-day timeframe. The outline the key scenarios of the applicability of platform-supported experiments and reflect on lessons learned from this two-year project that can help researchers and practitioners to integrate adaptive experiments in real-world courses

2025-03-02

Proceedings of the 15th International Learning Analytics and Knowledge Conference (publié)

doi.org

DialEgg: Dialect-Agnostic MLIR Optimizer using Equality Saturation with Egglog.

Abd-El-Aziz Zayed

Christophe Dubach

MLIR’s ability to optimize programs at multiple levels of abstraction is key to enabling domain-specific optimizing compilers. However, ex… (voir plus)pressing optimizations remains tedious. Optimizations can interact in unexpected ways, making it hard to unleash full performance. Equality saturation promises to solve these challenges. First, it simplifies the expression of optimizations using rewrite rules. Secondly, it considers all possible optimization interactions, through saturation, selecting the best program variant. Despite these advantages, equality saturation remains absent from production compilers such as MLIR. This paper proposes to integrate Egglog, a recent equality saturation engine, with MLIR, in a dialect-agnostic manner. This paper shows how the main MLIR constructs such as operations, types or attributes can be modeled in Egglog. It also presents DialEgg, a tool that pre-defines a large set of common MLIR constructs in Egglog and automatically translates between the MLIR and Egglog program representations. Using a few use-cases, this paper demonstrates the potential for combining equality saturation and MLIR.

2025-02-28

IEEE/ACM International Symposium on Code Generation and Optimization (publié)

doi.org

Divergent responses to SARS-CoV-2 infection in bronchial epithelium with pre-existing respiratory diseases

Justine Oliva

Manon Ruffin

Claire Calmel

Aurélien Gibeaud

Andrés Pizzorno

Clémence Gaudin

Solenne Chardonnet

Viviane de Almeida Bastos

Manuel Rosa-Calatrava

Antoine Soulé

Amin Emad

Simon Rousseau

Harriet Corvol

Olivier Terrier

Loïc Guillot

2025-02-28

iScience (publié)

doi.org

Implicit Generative Modeling by Kernel Similarity Matching

Shubham Choudhary

Paul Masset

Demba Ba

2025-02-28

ArXiv (prépublication)

arxiv.org

Improving clustering quality evaluation in noisy Gaussian mixtures

Renato Cordeiro De Amorim

Vladimir Makarenkov

2025-02-28

ArXiv (prépublication)

doi.org

arxiv.org

Interpretable deep learning for deconvolutional analysis of neural signals

Bahareh Tolooshams

Sara Matias

Hao Wu

Simona Temereanca

Naoshige Uchida

Venkatesh N. Murthy

Paul Masset

Demba Ba

The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on “black-box… (voir plus) approaches that lack an interpretable link between neural activity and network parameters. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the heterogeneity of neural responses in the piriform cortex and in the striatum during unstructured, naturalistic experiments. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural activity.

2025-02-28

Neuron (publié)

doi.org

Large language models deconstruct the clinical intuition behind diagnosing autism

Emmett Rabot

Laurent Mottron

2025-02-28

Cell (publié)

doi.org

Learning adversarially robust kernel ensembles with kernel average pooling.

Pouya Bashivan

Reza Bayat

Adam Ibrahim

Amirozhan Dehghani

Yifei Ren

2025-02-28

Expert Systems with Applications (publié)

doi.org

A Multi-Robot Exploration Planner for Space Applications

Vivek Shankar Vardharajan

Giovanni Beltrame

We propose a distributed multi-robot exploration planning method designed for complex, unconstrained environments featuring steep elevation … (voir plus)changes. The method employs a two-tiered approach: a local exploration planner that constructs a grid graph to maximize exploration gain and a global planner that maintains a sparse navigational graph to track visited locations and frontier information. The global graphs are periodically synchronized among robots within communication range to maintain an updated representation of the environment. Our approach integrates localization loop closure estimates to correct global graph drift. In simulation and field tests, the proposed method achieves 50% lower computational runtime compared to state-of-the-art methods while demonstrating superior exploration coverage. We evaluate its performance in two simulated subterranean environments and in field experiments at a Mars-analog terrain.

2025-02-28

IEEE Robotics and Automation Letters (publié)

doi.org

Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.

Sandrine Bédard

Jan Valosek

Maryam Seif

Armin Curt

Simon Schading-Sassenhausen

Nikolai Pfender

P. Freund

Markus Hupp