Publications

Facing AI extinction

David Scott Krueger

2023-04-01

New Scientist (published)

Genesis, modelling and methodological remedies to autism heterogeneity

Juliette Rabot

Eya‐mist Rødgaard

Ridha Joober

Guillaume Dumas

Boris C Bernhardt

Sébastien Jacquemont

Laurent Mottron

2023-04-01

Neuroscience and Biobehavioral Reviews (published)

Home alone: A population neuroscience investigation of brain morphology substrates

M. Noonan

Chris Zajner

2023-04-01

NeuroImage (published)

Investigating the neural correlates of affective mentalizing and their association with general intelligence in patients with schizophrenia

Wladimir Tantchik

M. J. Green

Yann Quidé

Susanne Erk

Sebastian Mohnke

Carolin Wackerhagen

Nina Romanczuk-seiferth

Heike Tost

Kristina Schwarz

Carolin Moessnang

Andreas Meyer-Lindenberg

Andreas Heinz

Henrik Walter

2023-04-01

Schizophrenia Research (published)

Machine-learning-based arc selection for constrained shortest path problems in column generation

Mouad Morabit

Guy Desaulniers

Andrea Lodi

Column generation is an iterative method used to solve a variety of optimization problems. It decomposes the problem into two parts: a maste… (see more)r problem and one or more pricing problems (PP). The total computing time taken by the method is divided between these two parts. In routing or scheduling applications, the problems are mostly defined on a network, and the PP is usually an NP-hard shortest path problem with resource constraints. In this work, we propose a new heuristic pricing algorithm based on machine learning. By taking advantage of the data collected during previous executions, the objective is to reduce the size of the network and accelerate the PP, keeping only the arcs that have a high chance to be part of the linear relaxation solution. The method has been applied to two specific problems: the vehicle and crew scheduling problem in public transit and the vehicle routing problem with time windows. Reductions in computational time of up to 40% can be obtained.

2023-04-01

INFORMS Journal on Optimization (published)

Picture Cards Versus Physical Examination: A Proof-of-Concept Study to Improve the SOSAS Survey Tool.

Adesoji Ademuyiwa

Benedict C. Nwomeh

Dan Poenaru

Justina O. Seyi-Olajide

Iyabo Y. Ademuyiwa

Tinuola O. Odugbemi

Ogechi Abazie

Oluwaseun A. Ladipo-Ajayi

Olufemi Bankole

Olumide A. Elebute

Babasola Okusanya

Felix M. Alakaloko

Eyitayo O. Alabi

Ayomide Makanjuola

Shailvi Gupta

Tu Tran

Amanda Onwuka A

Emily R. Smith

Riinu Pius

Ewen Harrison … (see 1 more)

Christopher O. Bode

2023-04-01

Journal of Surgical Research (published)

A Survey of Self-Supervised and Few-Shot Object Detection

Gabriel Huang

Issam Hadj Laradji

David Vazquez

Simon Lacoste-Julien

Pau Rodriguez

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require … (see more)dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.

2023-04-01

IEEE Transactions on Pattern Analysis and Machine Intelligence (published)

Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests

Christopher Beckham

Martin Weiss

Florian Golemo

Sina Honari

Derek Nowrouzezahrai

Chris Pal

2023-04-01

Pattern Recognition (published)

openreview.net

Bayesian stroke modeling details sex biases in the white matter substrates of aphasia

Julius M Kernbach

Gesa Hartwigsen

Jae‐Sung Lim

Hee-Joon Bae

Kyung‐Ho Yu

Gottfried Schlaug

Anna K. Bonkhoff

Natalia S. Rost

2023-03-31

Communications Biology (published)

MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis

Mohammad Khodadad

Morteza Rezanejad

Ali Shiraee Kasmaee

Kaleem Siddiqi

Dirk Bernhardt-Walther

Hamidreza Mahyar

2023-03-31

ArXiv (preprint)

Varepsilon kú mask: Integrating Yorùbá cultural greetings into machine translation

Idris Akinade

Jesujoba Oluwadara Alabi

David Ifeoluwa Adelani

Clement Odoje

Dietrich Klakow

This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings… (see more) (kú mask), which are a big part of Yorùbá language and culture, into English. To evaluate these models, we present IkiniYorùbá, a Yorùbá-English translation dataset containing some Yorùbá greetings, and sample use cases. We analysed the performance of different multilingual NMT systems including Google and NLLB and show that these models struggle to accurately translate Yorùbá greetings into English. In addition, we trained a Yorùbá-English model by fine-tuning an existing NMT model on the training split of IkiniYorùbá and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data.

2023-03-31

ArXiv (preprint)