Publications

α-ReQ: Assessing representation quality by measuring eigenspectrum decay
Kumar Krishna Agrawal
Arnab Kumar Mondal
Arna Ghosh
Blake A. Richards
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)
openreview.net
openreview.net
Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability
Avinash Bhat
Austin Coursey
Grace Hu
Sixian Li
Nadia Nahar
Shurui Zhou
Christian Kästner
Jin L.C. Guo
2021-12-31
(publié)
doi.org
Assessing the Quality of Direct-to-Consumer Teleconsultation Services in Canada
Jean Noel Nikiema
Eleah Stringer
Marie-Pierre Moreault
Priscille Pana
Marco Laverdiere
Catherine Régis
Jean-Louis Denis
Béatrice Godard
Mylaine Breton
Guy Paré
Aviv Shachak
Claudia Lai
Elizabeth M. Borycki
Andre W. Kushniruk
Aude Motulsky
The objective of this study was to describe and assess the quality of the direct-to-consumer medical teleconsultation landscape in three Can… (voir plus)adian provinces. An environmental scan of primary care teleconsultation platforms was conducted in January 2022 to identify medical teleconsultation platforms in Quebec (Qc), Ontario, and British Columbia (BC). The quality of each teleconsultation platform was assessed using a modified version of the HONcode principles. Nineteen different direct-to-consumer medical teleconsultation platforms were identified across the three provinces. The quality of these teleconsultation platforms was very heterogeneous. The landscape of virtual primary care is changing rapidly in the Canadian ecosystem, and the transparency of current teleconsultation platforms could be improved.
2021-12-31
Medical Informatics Europe (publié)
doi.org
Attention-based Neural Cellular Automata
Mattie Tesfaldet
Derek Nowrouzezahrai
Christopher Pal
Recent extensions of Cellular Automata (CA) have incorporated key ideas from modern deep learning, dramatically extending their capabilities… (voir plus) and catalyzing a new family of Neural Cellular Automata (NCA) techniques. Inspired by Transformer-based architectures, our work presents a new class of
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)
doi.org
openreview.net
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Georg Northoff
Maia Fraser
John Griffiths
Dimitris A. Pinotsis
Prakash Panangaden
Rosalyn Moran
Karl Friston
Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for f… (voir plus)unctional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.
2021-12-31
Frontiers in Computational Neuroscience (publié)
doi.org
Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion
Matthew Farrell
Stefano Recanatesi
Timothy Moore
Guillaume Lajoie
Eric Shea-Brown
2021-12-31
Nature Machine Intelligence (publié)
doi.org
Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention
Leo Schwinn
Doina Precup
B. Eskofier
Dario Zanca
.
2021-12-31
arXiv.org (prépublication)
doi.org
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
Leo Schwinn
Doina Precup
Bjoern Eskofier
Dario Zanca
By and large, existing computational models of visual attention tacitly assume perfect vision and full access to the stimulus and thereby de… (voir plus)viate from foveated biological vision. Moreover, modeling top-down attention is generally reduced to the integration of semantic features without incorporating the signal of a high-level visual tasks that have been shown to partially guide human attention. We propose the Neural Visual Attention (NeVA) algorithm to generate visual scanpaths in a top-down manner. With our method, we explore the ability of neural networks on which we impose a biologically-inspired foveated vision constraint to generate human-like scanpaths without directly training for this objective. The loss of a neural network performing a downstream visual task (i.e., classification or reconstruction) flexibly provides top-down guidance to the scanpath. Extensive experiments show that our method outperforms state-of-the-art unsupervised human attention models in terms of similarity to human scanpaths. Additionally, the flexibility of the framework allows to quantitatively investigate the role of different tasks in the generated visual behaviors. Finally, we demonstrate the superiority of the approach in a novel experiment that investigates the utility of scanpaths in real-world applications, where imperfect viewing conditions are given.
2021-12-31
Trans. Mach. Learn. Res. (publié)
openreview.net
openreview.net
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Yuhan Helena Liu
Arna Ghosh
Blake A. Richards
Eric Shea-Brown
Guillaume Lajoie
To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurre… (voir plus)nt neural networks (RNNs). Yet, beyond task accuracy, it is unclear if such learning rules converge to solutions that exhibit different levels of generalization than their nonbiologically-plausible counterparts. Leveraging results from deep learning theory based on loss landscape curvature, we ask: how do biologically-plausible gradient approximations affect generalization? We first demonstrate that state-of-the-art biologically-plausible learning rules for training RNNs exhibit worse and more variable generalization performance compared to their machine learning counterparts that follow the true gradient more closely. Next, we verify that such generalization performance is correlated significantly with loss landscape curvature, and we show that biologically-plausible learning rules tend to approach high-curvature regions in synaptic weight space. Using tools from dynamical systems, we derive theoretical arguments and present a theorem explaining this phenomenon. This predicts our numerical results, and explains why biologically-plausible rules lead to worse and more variable generalization properties. Finally, we suggest potential remedies that could be used by the brain to mitigate this effect. To our knowledge, our analysis is the first to identify the reason for this generalization gap between artificial and biologically-plausible learning rules, which can help guide future investigations into how the brain learns solutions that generalize.
2021-12-31
NeurIPS (publié)
doi.org
openreview.net
Biasly: a machine learning based platform for automatic racial discrimination detection in online texts
David Bamman
Chris Dyer
Noah A. Smith. 2014
Steven Bird
Ewan Klein
Edward Loper
Nat-527
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova. 2019
Bert
Samuel Gehman
Suchin Gururangan
Maarten Sap
Dan Hendrycks
Kevin Gimpel. 2020
Gaussian
Alex Lamb
Di He … (voir 22 de plus)
Anirudh Goyal
Guolin Ke
Feng-Ju Liao
Mirco Ravanaelli
Yoshua Bengio
Zhenzhong Lan
Mingda Chen
Sebastian Goodman
Yann Lecun
Bernhard E. Boser
J. Denker
Don-608 nie Henderson
Robin Howard
Wayne Hubbard
Yinhan Liu
Myle Ott
Naman Goyal
Jingfei Du
Mandar Joshi
Danqi Chen
Omer Levy
Mike Lewis
Warning : this paper contains content that may 001 be offensive or upsetting. 002 Detecting hateful, toxic, and otherwise racist 003 or sexi… (voir plus)st language in user-generated online con-004 tents has become an increasingly important task 005 in recent years. Indeed, the anonymity, the 006 transience, the size of messages, and the dif-007 ficulty of management, facilitate the diffusion 008 of racist or hateful messages across the Inter-009 net. The critical influence of this cyber-racism 010 is no longer limited to social media, but also 011 has a significant effect on our society : corpo-012 rate business operation, users’ health, crimes, 013 etc. Traditional racist speech reporting chan-014 nels have proven inadequate due to the enor-015 mous explosion of information, so there is an 016 urgent need for a method to automatically and 017 promptly detect texts with racial discrimination. 018 We propose in this work, a machine learning-019 based approach to enable automatic detection 020 of racist text content over the internet. State-of-021 the-art machine learning models that are able 022 to grasp language structures are adapted in this 023 study. Our main contribution include 1) a large 024 scale racial discrimination data set collected 025 from three distinct sources and annotated ac-026 cording to a guideline developed by specialists, 027 2) a set of machine learning models with vari-028 ous architectures for racial discrimination de-029 tection, and 3) a web-browser-based software 030 that assist users to debias their texts when us-031 ing the internet. All these resources are made 032 publicly available.
2021-12-31
(publié)
www.semanticscholar.org
Bisimulation metrics and norms for real-weighted automata
Borja Balle
Pascale Gourdeau
Prakash Panangaden
2021-12-31
Information and Computation (publié)
doi.org
Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study
Ali Khatibi
Shahabeddin Vahdat
Ovidiu Lungu
Jürgen Finsterbusch
Christian Büchel
Julien Cohen-Adad
Veronique Marchand-Pauvert
Julien Doyon
2021-12-31
NeuroImage (publié)
doi.org