Publications

Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy

Jos'e-Fabian Villa-V'asquez

Unsupervised object discovery is commonly interpreted as the task of localizing and/or categorizing objects in visual data without the need … (see more)for labeled examples. While current object recognition methods have proven highly effective for practical applications, the ongoing demand for annotated data in real-world scenarios drives research into unsupervised approaches. Furthermore, existing literature in object discovery is both extensive and diverse, posing a significant challenge for researchers that aim to navigate and synthesize this knowledge. Motivated by the evidenced interest in this avenue of research, and the lack of comprehensive studies that could facilitate a holistic understanding of unsupervised object discovery, this survey conducts an in-depth exploration of the existing approaches and systematically categorizes this compendium based on the tasks addressed and the families of techniques employed. Additionally, we present an overview of common datasets and metrics, highlighting the challenges of comparing methods due to varying evaluation protocols. This work intends to provide practitioners with an insightful perspective on the domain, with the hope of inspiring new ideas and fostering a deeper understanding of object discovery approaches.

2024-10-31

arXiv (published)

doi.org

arxiv.org

Improving Adversarial Transferability via Model Alignment

Avery Ma

Amir-massoud Farahmand

Yangchen Pan

Philip Torr

Jindong Gu

Neural networks are susceptible to adversarial perturbations that are transferable across different models. In this paper, we introduce a no… (see more)vel model alignment technique aimed at improving a given source model's ability in generating transferable adversarial perturbations. During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss. This loss measures the divergence in the predictions between the source model and another, independently trained model, referred to as the witness model. To understand the effect of model alignment, we conduct a geometric analysis of the resulting changes in the loss landscape. Extensive experiments on the ImageNet dataset, using a variety of model architectures, demonstrate that perturbations generated from aligned source models exhibit significantly higher transferability than those from the original source model.

2024-10-30

Lecture Notes in Computer Science (published)

doi.org

arxiv.org

Unclocklike biological oscillators with frequency memory

Christian Mauffette Denis

Paul François

Entrainment experiments on the vertebrate segmentation clock have revealed that embryonic oscillators actively change their internal frequen… (see more)cy to adapt to the driving signal. This is not consistent with either a one-dimensional clock model or a limit-cycle model, but rather it suggests a new “unclocklike” behavior. In this work, we propose simple, biologically realistic descriptions of such internal frequency adaptation, where a phase oscillator activates a memory variable controlling the oscillator's frequency. We study two opposite limits for the control of the memory variable, one with a smooth phase-averaging memory field, and the other with a pulsatile, phase-dependent activation. Both models recapitulate intriguing properties of the entrained segmentation clock, such as very broad Arnold tongues and an entrainment phase plateauing with detuning. We compute analytically multiple properties of such systems, such as entrainment phases and cycle shapes. We further describe new phenomena, including hysteresis in entrainment, bistability in the frequency of the entrained oscillator, and probabilistic entrainment. Our work shows that oscillators with frequency memory can exhibit new classes of unclocklike properties that can be tested through experimental entrainment. Published by the American Physical Society 2024

2024-10-30

Physical Review X Life (published)

doi.org

arxiv.org

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Andrea Maccio

Yashar Hezaveh

2024-10-29

NeurIPS.cc/2024/Workshop/CRL (poster)

openreview.net

General Causal Imputation via Synthetic Interventions

Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their inte… (see more)ractions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.

2024-10-29

NeurIPS.cc/2024/Workshop/CRL (poster)

doi.org

openreview.net

From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems

Shalaleh Rismani

Roel Dobbe

AJung Moon

2024-10-28

ArXiv (preprint)

doi.org

arxiv.org

Injury and violence in the context of sustainable development

Kidist Bartolomeos

Ryan Lett

Berjo Takoutsing

Respicious Boniface

Victoria Munthali

Tarek Razek

Dan Deckelbaum

David Bracco

Ermiyas Belay

Fitsum Kifle

David Ulrich Dalle

Celestin Bilong Mbangtang

Arsene Daniel Nyalundja

Jondre Macaraeg

Irene Dzirasa

Ulrick Sidney Kanmounye

Delanyo Dovlo

Kwadwo Koram

Eugene Nyarko

Desmond T. Jumbam … (see 185 more)

Emnet Tesfay Shimber

Taylor Jaraczewski

Shemsedin Ibro

Maria Sgro

Ajiel Mae Basmayor

Asegid Ergete

Mary Schroeder

Adam Gyedu

Emmanuel Nakua

Peter Donkor

Charles Mock

Atalel Awedew

Halid Melkamu

Sisay Bekele

Berhanu Hailemariam

Enku Shiferaw

Yishak Shiferaw

Wubetie Yirdaw

Debojit Basak

Deepa Kizhakke Veetil

Nobhojit Roy

Martin Gerdin Wärnberg

Santosh Rath

Mohammed A.S Abdullahi

Kefas Mbaya

Abubakar Kakasanda

Stephanie Danjuma

Hector Olasoji

Alemayehu Bedada

Mpapho Joseph Motsumi

Shimelis Genna Hamda

Demuma Amdisa

Getachew Tilahun

Katie Iverson

Matthew Boroditsky

Mark Hill

Roy Hilzenrat

Rachel Livergant

Jayd Adams

Catherine Binda

Allison Chhor

Helen Hsiao

Faizal Haji

Esther Chin

Felix Oyania

Caroline Q. Stephens

Sarah Ullrich

Meera Kotagal

Francis Bajunirwe

Doruk Ozgediz

Dionysia Kravarioti

Lye-Yeng Wong

Tsegazeab Laeke Teklemariam

Abenezer Tirsit

Tewodros Liyew

Mark Ferguson

Timothy Plackett

Jaymie Claire Henry

Meseret Abeza

Seye Mesfin Minas

Maryse Bouchard

Dimuthu Tennakoon

Rahul Burra

Fleming Mathew

Annabelle Jones

Sargun Virk

Shlok Patel

Tanaz Vaghaiwalla

James Hudspeth

Tracy Rabin

Virginia Rowthorn

Raymond R. Price

Nakul Raykar

Gilgamesh Eamer

Stephen Mutiso

Yvette Kisaka

Gladwell Gathecha

Ronald Lett

Chibuike Onu

Emmanuel Ameh

Matthias Igoche

Meseret Abeza

Paschal Anyanwu

Eunice Onuh

Oikeh Ojeamen

Edith Terna Yawe

Amina Abubakar

Yakubu Ashoms

Hadiza Suleiman

Naomi Musa

Daniel Kisitu Kyengera

Netsanet Abebe

Richard Gardener

Nebyou Seyoum Abebe

Henok T/Silasie Zeleke

Kacylia Roy Proulx

Shreenik Kundu

Boaz Laor

Riya Sawhney

Taylor Wurdeman

Fabio Botelho

Ayla Gerk

Elena Guadagno

Dan Poenaru

Mengistu Ayele

Azarias Kassahun

Tsegazeab Laeke

Mestet Yibeltal

Bereket Hailu

Ermias Fikru

Shemsedin Ibro

Abdeta Workineh

Fikadu Balcha

Fira Abamecha

Sheka Shemsi

Abdullah Saleh Alruwaili

Gabriel Rodriguez

Anna Jose

Shahd Ebied

Samuel Girma

Abigael Abiy

Hussien Endris Assen

Kalab Tesfaye

Kassaye Demeke

Aklilu Yiheyis

Khalid Jemal

Demeke Yilkal

Ashenafi Amsalu

Lema Derseh

Yophtahe W/Gerima

Tadesse Belayneh

Mekuanint Tiruneh

Almaw Bitew

Sewbesew Yitayih

Tadesse Awoke

Chanyalew Worku

Anissa Mohammed

Mohammed Alemu

Mohammed Yesuf

Fantu Mamo

Kegnie Shitu

Biks Liyew

Ayenew Gucho

Gezahegn Tilahun

Timothy Love

Andrew Chew

Brian Kasagga

Berjo Takoutsing

Obuku Ekwaro

Emmanuel Elobu

Degisew Dersso Mengistu

Alex Zhuang

Bethlehem Shiferew

Gelila Mengistu

Ayalew Zewdie

Nahom Tadelle

Alegnta Gebreyesus

Elise Presser

Katie Iverson

Christopher Dodgion

Thomas G. Weiser

Rachel Koch

Nichole Starr

Davy Lau

Irena Zivkovic

Christopher Dodgion

Shahrzad Joharifard

Emilie Joos

Naisan Garraway

Francesca Vituci

Eric O’Flynn

Ines Péric

Léa Simon

Geoffrey Ibbotson

Tsion Seyoum

Aklilu Azazh

Lemlem Beza

Ifeanyichukwu Onah

Chijioke Chukwuma

Dagim Berhanu

Jason Shenoi

Nick Sears

Yoseph Bedore

Richard Caplan

Wongel Tena Shale

invaluable

2024-10-28

Canadian journal of surgery. Journal canadien de chirurgie (published)

doi.org

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

Julien Pallage

Bertrand Scherrer

Salma Naccache

Christophe B'elanger

Antoine Lesage-Landry

2024-10-28

ArXiv (preprint)

doi.org

arxiv.org

The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa

Mercy Nyamewaa Asiedu

Awa Dieng

Iskandar Haykel

Negar Rostamzadeh

Stephen R. Pfohl

Chirag Nagpal

Maria Nagawa

Abigail Oppong

Sanmi Koyejo

Katherine Heller

With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand … (see more)and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances between the Global North and South.This paper seeks to explore fairness for global health, with Africa as a case study. We conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.

2024-10-28

Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (published)

doi.org

arxiv.org

Doctoral Symposium Committee

Anthony Cleve

Christian Lange

Silvia Breu

Manar H. Alalfi

Mario Luca Bernardi

Cornelia Boldyreff

Marco D'Ambros

Simon Denier

Natalia Dragan

Ekwa Duala-Ekoko

Fausto Fasano

Adnane Ghannem

Carmine Gravino

Maen Hammad

Imed Hammouda

Salima Hassaine

Yue Jia

Zhen Ming (Jack) Jiang

Foutse Khomh

Adam Kiezun … (see 11 more)

Jay Kothari

Jonathan Memaitre

Naouel Moha

Rocco Oliveto

Denys Poshyvanyk

Michele Risi

Giuseppe Scanniello

Bonita Sharif

Andrew Sutton

Anis Yousefi

Eugenio Zimeo

Manar H. Alalfi Mario Luca Bernardi Cornelia Boldyreff Anthony Cleve Marco D'Ambros Simon Denier Natalia Dragan Ekwa Duala-Ekoko Fausto Fasa… (see more)no Adnane Ghannem Carmine Gravino Maen Hammad Imed Hammouda Salima Hassaine Yue Jia Zhen Ming Jiang Foutse Khomh Adam Kiezun Jay Kothari Jonathan Memaitre Naouel Moha Rocco Oliveto Denys Poshyvanyk Michele Risi Giuseppe Scanniello Bonita Sharif Andrew Sutton Anis Yousefi Eugenio Zimeo

2024-10-27

2024 IEEE 35th International Symposium on Software Reliability Engineering Workshops (ISSREW) (published)

doi.org

Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation

Michael Przystupa

Gauthier Gidel

Matthew E. Taylor

Martin Jagersand

Justus Piater

Samuele Tosatto

As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay peop… (see more)le be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF manipulators -- prior research suggests these mappings can simplify the teleoperation experience for users. Recent works suggest that neural networks predicting a local linear function are superior to the typical end-to-end multi-layer perceptrons because they allow users to more easily undo actions, providing more control over the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced actions in training data. We observe that the benefit of these mappings is being an odd function concerning user actions, and propose end-to-end nonlinear action maps which achieve this property. Unfortunately, our experiments show that such modifications offer minimal advantages over previous solutions. We find that nonlinear odd functions behave linearly for most of the control space, suggesting architecture structure improvements are not the primary factor in data-driven teleoperation. Our results suggest other avenues, such as data augmentation techniques and analysis of human behavior, are necessary for action maps to become practical in real-world applications, such as in assistive robotics to improve the quality of life of people living with w disability.

2024-10-27

ArXiv (preprint)

doi.org

arxiv.org

Trajectory Flow Matching with Applications to Clinical Time Series Modeling

Xi Zhang

Yuan Pu

Yuki Kawamura

Andrew Loza

Yoshua Bengio

Dennis L. Shung

Alexander Tong