Publications

Increasing schedule reliability in the multiple depot vehicle scheduling problem with stochastic travel time
L'ea Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau
2024-03-31
Omega (published)
doi.org
Machine Learning Robustness: A Primer
Houssem Ben Braiek
Foutse Khomh
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness … (see more)in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.
2024-03-31
ArXiv (preprint)
doi.org
arxiv.org
Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalisable improvements in brain encoding and downstream tasks
Maelle Freteault
Loic Tetrel
Maximilien Le Clei
Lune P Bellec
Nicolas Farrugia
Artificial neural networks trained in the field of artificial intelligence (AI) have emerged as key tools to model brain processes, sparking… (see more) the idea of aligning network representations with brain dynamics to enhance performance on AI tasks. While this concept has gained support in the visual domain, we investigate here the feasibility of creating auditory artificial neural models directly aligned with individual brain activity. This objective raises major computational challenges, as models have to be trained directly with brain data, which is typically collected at a much smaller scale than data used to train AI models. We aimed to answer two key questions: (1) Can brain alignment of auditory models lead to improved brain encoding for novel, previously unseen stimuli? (2) Can brain alignment lead to generalisable representations of auditory signals that are useful for solving a variety of complex auditory tasks? To answer these questions, we relied on two massive datasets: a deep phenotyping dataset from the Courtois neuronal modelling project, where six subjects watched four seasons (36 hours) of the Friends TV series in functional magnetic resonance imaging and the HEAR benchmark, a large battery of downstream auditory tasks. We fine-tuned SoundNet, a small pretrained convolutional neural network with ∼2.5M parameters. Aligning SoundNet with brain data from three seasons of Friends led to substantial improvement in brain encoding in the fourth season, extending beyond auditory and visual cortices. We also observed consistent performance gains on the HEAR benchmark, particularly for tasks with limited training data, where brain-aligned models performed comparably to the best-performing models regardless of size. We finally compared individual and group models, finding that individual models often matched or outperformed group models in both brain encoding and downstream task performance, highlighting the data efficiency of fine-tuning with individual brain data. Our results demonstrate the feasibility of aligning artificial neural network representations with individual brain activity during auditory processing, and suggest that this alignment is particularly beneficial for tasks with limited training data. Future research is needed to establish whether larger models can achieve even better performance and whether the observed gains extend to other tasks, particularly in the context of few shot learning.
2024-03-30
bioRxiv (preprint)
doi.org
Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots
Chao Shen
Wenkang Zhan
Kaiyao Xin
Manyang Li
Zhenyu Sun
Hui Cong
Chi Xu
Jian Tang
Zhaofeng Wu
Bo Xu
Zhongming Wei
Chunlai Xue
Chao Zhao
Zhanguo Wang
2024-03-28
Nature Communications (published)
doi.org
arxiv.org
Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
Sana Ahmadi
Lune P Bellec
Tristan Glatard
2024-03-27
ArXiv (preprint)
doi.org
arxiv.org
Fast burst fraction transients convey information independent of the firing rate
Richard Naud
Xingyun Wang
Zachary Friedenberger
Alexandre Payeur
Jiyun N Shin
Jean-Claude Beique
Blake Richards
Moritz Drüke
Matthew Larkum
Guy Doron
Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental rep… (see more)orts of burst-mediated representations \emph{in vivo} have been limited. Here we used a novel demultiplexing approach by considering a conjunctive burst code. We studied this code \emph{in vivo} while animals learned to report direct electrical stimulation of the somatosensory cortex and found two acquired yet independent representations. One code, the event rate, showed a sparse and succint stiumulus representation and a small modulation upon detection errors. The other code, the burst fraction, correlated more globally with stimulation and more promptly responded to detection errors. Potent and fast modulations of the burst fraction were seen even in cells that were considered unresponsive based on the firing rate. During the later stages of training, this modulation in bursting happened earlier, gradually aligning temporally with the representation in event rate. The alignment of bursting and event rate modulation sharpened the firing rate response, and was strongly associated with behavioral accuracy. Thus a fine-grained separation of spike timing patterns reveals two signals that accompany stimulus representations: an error signal that can be essential to guide learning and a sharpening signal that could implement attention mechanisms.
2024-03-26
bioRxiv (preprint)
doi.org
Predicting Species Occurrence Patterns from Partial Observations
Hager Radi Abdelwahed
Mélisande Teng
David Rolnick
To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are chang… (see more)ing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.
2024-03-25
arXiv (preprint)
doi.org
arxiv.org
On quasi-homomorphism rigidity for lattices in simple algebraic groups
Guillaume Dumas
2024-03-24
ArXiv (preprint)
arxiv.org
arxiv.org
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
Hi Bn
Ramakrishna Appicharla
Kamal Kumar
Asif Gupta
Dzmitry Bahdanau
Kyunghyun Cho
Yoshua Ben­
Ondrej Bojar
Christian Buck
Christian Federmann
Yong Cheng
Lu Jiang
Wolfgang Macherey
Alexis Conneau
Guillaume Lample. 2019
Cross­
Yinhan Liu
Jiatao Gu
Naman Goyal
Sergey Xian Li … (see 45 more)
Carol Myers­Scotton. 1997
El Moatez
Billah Nagoudi
AbdelRahim Elmadany
Muhammad Abdul­Mageed. 2021. Investigat­
Myle Ott
Sergey Edunov
Alexei R Baevski
Parth Patwa
Gustavo Aguilar
Sudipta Kar
Suraj
Srinivas Pandey
Björn Pykl
Gambäck
Tanmoy
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
dukasz Kaiser
Illia Polosukhin. 2017
Attention
Genta Indra Winata
Andrea Madotto
Chien­Sheng
Wu Pascale
Fung
Code­switching
ing. In
Felix Wu
Angela Fan
Yann Dauphin
Linting Xue
Noah Constant
Mihir Adam Roberts
Rami Kale
Aditya Al­Rfou
Aditya Siddhant
Barua
Shuyan Zhou
Xiangkai Zeng
Antonios Yingqi Zhou
Anastasopoulos Graham
Neubig. 2019
Im­
The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixe… (see more)d language) in a single utterance. This has resulted a formidable challenge for the computational models due to the scarcity of annotated data and presence of noise. A potential solution to mitigate the data scarcity problem in low-resource setup is to leverage existing data in resource-rich language through translation. In this paper, we tackle the problem of code-mixed (Hinglish and Bengalish) to English machine translation. First, we synthetically develop HINMIX, a parallel corpus of Hinglish to English, with ~4.2M sentence pairs. Subsequently, we propose RCMT, a robust perturbation based joint-training model that learns to handle noise in the real-world code-mixed text by parameter sharing across clean and noisy words. Further, we show the adaptability of RCMT in a zero-shot setup for Bengalish to English translation. Our evaluation and comprehensive analyses qualitatively and quantitatively demonstrate the superiority of RCMT over state-of-the-art code-mixed and robust translation methods.
2024-03-24
ArXiv (preprint)
doi.org
arxiv.org
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
Geraldin Nanfack
Alexander Fulleringer
Jonathan Marty
Michael Eickenberg
Eugene Belilovsky
Feature visualization is one of the most popular techniques used to interpret the internal behavior of individual units of trained deep neur… (see more)al networks. Based on activation maximization, they consist of finding synthetic or natural inputs that maximize neuron activations. This paper introduces an optimization framework that aims to deceive feature visualization through adversarial model manipulation. It consists of finetuning a pre-trained model with a specifically introduced loss that aims to maintain model performance, while also significantly changing feature visualization. We provide evidence of the success of this manipulation on several pre-trained models for the classification task with ImageNet.
2024-03-23
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Generalizing across Temporal Domains with Koopman Operators
Qiuhao Zeng
Wei Wang
Fan Zhou
Gezheng Xu
Ruizhi Pu
Changjian Shui
Christian Gagné
Shichun Yang
Boyu Wang
Charles Ling
2024-03-23
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Improving Automatic VQA Evaluation Using Large Language Models
Oscar Mañas
Benno Krojer
Aishwarya Agrawal
8 years after the visual question answering (VQA) task was proposed, accuracy remains the primary metric for automatic evaluation. VQA Accur… (see more)acy has been effective so far in the IID evaluation setting. However, our community is undergoing a shift towards open-ended generative models and OOD evaluation. In this new paradigm, the existing VQA Accuracy metric is overly stringent and underestimates the performance of VQA systems. Thus, there is a need to develop more robust automatic VQA metrics that serve as a proxy for human judgment. In this work, we propose to leverage the in-context learning capabilities of instruction-tuned large language models (LLMs) to build a better VQA metric. We formulate VQA evaluation as an answer-rating task where the LLM is instructed to score the accuracy of a candidate answer given a set of reference answers. We demonstrate the proposed metric better correlates with human judgment compared to existing metrics across several VQA models and benchmarks. We hope wide adoption of our metric will contribute to better estimating the research progress on the VQA task. We plan to release the evaluation code and collected human judgments.
2024-03-23
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org