Publications

Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
Sana Ahmadi
Lune Bellec
Tristan Glatard
2024-03-28
ArXiv (prépublication)
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 Béïque
Blake Richards
Moritz Drüke
Matthew E. Larkum
Guy Doron
Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental rep… (voir plus)orts of burst-mediated representations in vivo have been limited. Here we used a novel demultiplexing approach by considering a conjunctive burst code. We studied this code 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. Bursting modulation was potent and its time course evolved, 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 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.
2024-03-27
bioRxiv (prépublication)
doi.org
Application-Driven Innovation in Machine Learning
David Rolnick
Alan Aspuru-Guzik
Sara Beery
Bistra Dilkina
Priya L. Donti
Marzyeh Ghassemi
Hannah Kerner
Claire Monteleoni
Esther Rolf
Milind Tambe
Adam White
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly i… (voir plus)mportant. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.
2024-03-26
ArXiv (prépublication)
doi.org
arxiv.org
Predicting Species Occurrence Patterns from Partial Observations
Hager Radi
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… (voir plus)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.
2024-03-26
ArXiv (prépublication)
doi.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 … (voir 45 de plus)
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… (voir plus)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.
2024-03-25
ArXiv (prépublication)
doi.org
arxiv.org
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
G'eraldin 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… (voir plus)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.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
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
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
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… (voir plus)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.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread
Nicola Neophytou
Afaf Taïk
Golnoosh Farnadi
The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such dispa… (voir plus)rities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking
Pratheeksha Nair
Javin Liu
Catalina Vajiac
Andreas Olligschlaeger
Duen Horng Chau
Mirela T. Cazzolato
Cara Jones
Christos Faloutsos
Reihaneh Rabbany
Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions o… (voir plus)f people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically contain hundreds of thousands of ads including those posted by independent escorts, massage parlor agencies and spammers (fake ads). Detecting suspicious activity in these ads is difficult and developing data-driven methods is challenging due to the hard-to-label, complex and sensitive nature of the data. In this paper, we propose T-Net, which unlike previous solutions, formulates this problem as weakly supervised classification. Since it takes several months to years to investigate a case and obtain a single definitive label, we design domain-specific signals or indicators that provide weak labels. T-Net also looks into connections between ads and models the problem as a graph learning task instead of classifying ads independently. We show that T-Net outperforms all baselines on a real-world dataset of ads by 7% average weighted F1 score. Given that this data contains personally identifiable information, we also present a realistic data generator and provide the first publicly available dataset in this domain which may be leveraged by the wider research community.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking
Pratheeksha Nair
Javin Liu
Catalina Vajiac
Andreas Olligschlaeger
Duen Horng Chau
Mirela T. Cazzolato
Cara Jones
Christos Faloutsos
Reihaneh Rabbany
Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions o… (voir plus)f people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically contain hundreds of thousands of ads including those posted by independent escorts, massage parlor agencies and spammers (fake ads). Detecting suspicious activity in these ads is difficult and developing data-driven methods is challenging due to the hard-to-label, complex and sensitive nature of the data. In this paper, we propose T-Net, which unlike previous solutions, formulates this problem as weakly supervised classification. Since it takes several months to years to investigate a case and obtain a single definitive label, we design domain-specific signals or indicators that provide weak labels. T-Net also looks into connections between ads and models the problem as a graph learning task instead of classifying ads independently. We show that T-Net outperforms all baselines on a real-world dataset of ads by 7% average weighted F1 score. Given that this data contains personally identifiable information, we also present a realistic data generator and provide the first publicly available dataset in this domain which may be leveraged by the wider research community.
2024-03-24
AAAI Conference on Artificial Intelligence (publié)
doi.org