Publications

Learning from unexpected events in the neocortical microcircuit
Colleen J Gillon
Jason E. Pina
Jérôme A. Lecoq
Ruweida Ahmed
Yazan N. Billeh
Shiella Caldejon
Peter Groblewski
Timothy M. Henley
India Kato
Eric Lee
Jennifer Luviano
Kyla Mace
Chelsea Nayan
Thuyanh V. Nguyen
Kat North
Jed Perkins
Sam Seid
Matthew T. Valley
Ali Williford
Yoshua Bengio … (voir 3 de plus)
Timothy P. Lillicrap
Blake Richards
Joel Zylberberg
2023-11-21
Journal of Neuroscience (publié)
doi.org
Responses to Pattern-Violating Visual Stimuli Evolve Differently Over Days in Somata and Distal Apical Dendrites
Colleen J Gillon
Jason E. Pina
Jérôme A. Lecoq
Ruweida Ahmed
Yazan N. Billeh
Shiella Caldejon
Peter Groblewski
Timothy M. Henley
India Kato
Eric Lee
Jennifer Luviano
Kyla Mace
Chelsea Nayan
Thuyanh V. Nguyen
Kat North
Jed Perkins
Sam Seid
Matthew T. Valley
Ali Williford
Yoshua Bengio … (voir 3 de plus)
Timothy P. Lillicrap
Blake Richards
Joel Zylberberg
Scientists have long conjectured that the neocortex learns patterns in sensory data to generate top-down predictions of upcoming stimuli. In… (voir plus) line with this conjecture, different responses to pattern-matching vs pattern-violating visual stimuli have been observed in both spiking and somatic calcium imaging data. However, it remains unknown whether these pattern-violation signals are different between the distal apical dendrites, which are heavily targeted by top-down signals, and the somata, where bottom-up information is primarily integrated. Furthermore, it is unknown how responses to pattern-violating stimuli evolve over time as an animal gains more experience with them. Here, we address these unanswered questions by analyzing responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons tracked over multiple days in primary visual cortex of awake, behaving female and male mice. We use sequences of Gabor patches with patterns in their orientations to create pattern-matching and pattern-violating stimuli, and two-photon calcium imaging to record neuronal responses. Many neurons in both layers show large differences between their responses to pattern-matching and pattern-violating stimuli. Interestingly, these responses evolve in opposite directions in the somata and distal apical dendrites, with somata becoming less sensitive to pattern-violating stimuli and distal apical dendrites more sensitive. These differences between the somata and distal apical dendrites may be important for hierarchical computation of sensory predictions and learning, since these two compartments tend to receive bottom-up and top-down information, respectively.
2023-11-21
Journal of Neuroscience (publié)
doi.org
A Study of Human-Robot Handover through Human-Human Object Transfer
Charlotte Morissette
Bobak H. Baghi
Francois R. Hogan
Gregory Dudek
In this preliminary study, we investigate changes in handover behaviour when transferring hazardous objects with the help of a high-resoluti… (voir plus)on touch sensor. Participants were asked to hand over a safe and hazardous object (a full cup and an empty cup) while instrumented with a modified STS sensor. Our data shows a clear distinction in the length of handover for the full cup vs the empty one, with the former being slower. Sensor data further suggests a change in tactile behaviour dependent on the object's risk factor. The results of this paper motivate a deeper study of tactile factors which could characterize a risky handover, allowing for safer human-robot interactions in the future.
2023-11-21
ArXiv (prépublication)
doi.org
arxiv.org
Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation
Timothee LESORT
Oleksiy Ostapenko
Pau Rodriguez
Diganta Misra
Md Rifat Arefin
Laurent Charlin
Irina Rish
2023-11-20
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
proceedings.mlr.press
openreview.net
Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amin Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar
2023-11-20
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
doi.org
arxiv.org
An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software
Aaditya Bhatia
Foutse Khomh
Bram Adams
Ahmed E. Hassan
The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algori… (voir plus)thms with minimal overhead. However, during this accelerated ML development process, said developers may often make sub-optimal design and implementation decisions, leading to the introduction of technical debt that, if not addressed promptly, can have a significant impact on the quality of the ML-based software. Developers frequently acknowledge these sub-optimal design and development choices through code comments during software development. These comments, which often highlight areas requiring additional work or refinement in the future, are known as self-admitted technical debt (SATD). This paper aims to investigate SATD in ML code by analyzing 318 open-source ML projects across five domains, along with 318 non-ML projects. We detected SATD in source code comments throughout the different project snapshots, conducted a manual analysis of the identified SATD sample to comprehend the nature of technical debt in the ML code, and performed a survival analysis of the SATD to understand the evolution of such debts. We observed: i) Machine learning projects have a median percentage of SATD that is twice the median percentage of SATD in non-machine learning projects. ii) ML pipeline components for data preprocessing and model generation logic are more susceptible to debt than model validation and deployment components. iii) SATDs appear in ML projects earlier in the development process compared to non-ML projects. iv) Long-lasting SATDs are typically introduced during extensive code changes that span multiple files exhibiting low complexity.
2023-11-20
ArXiv (prépublication)
doi.org
arxiv.org
Responsible AI Research Needs Impact Statements Too
Alexandra Olteanu
Michael Ekstrand
Carlos Castillo
Jina Suh
All types of research, development, and policy work can have unintended, adverse consequences - work in responsible artificial intelligence … (voir plus)(RAI), ethical AI, or ethics in AI is no exception.
2023-11-20
ArXiv (prépublication)
doi.org
arxiv.org
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
Massimo Caccia
Jonas Mueller
Taesup Kim
Laurent Charlin
Rasool Fakoor
2023-11-20
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
proceedings.mlr.press
openreview.net
Tensor-based Space Debris Detection for Satellite Mega-constellations
Olivier Daoust
Hasan Nayir
Irfan Azam
Antoine Lesage-Landry
G. Kurt
2023-11-20
ArXiv (prépublication)
arxiv.org
arxiv.org
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
Hadi Nekoei
Xutong Zhao
Janarthanan Rajendran
Miao Liu
Sarath Chandar
2023-11-20
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
doi.org
arxiv.org
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar
Daniel Sumner Magruder
Edward De Brouwer
Matheo Morales
Aarthi Venkat
Frederik Wenkel
Guy Wolf
Smita Krishnaswamy
2023-11-18
logconference.io/LOG/2023/Conference (poster)
doi.org
openreview.net
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua
Sitao Luan
Minkai Xu
Zhitao Ying
Rex Ying
Jie Fu
Stefano Ermon
Doina Precup
2023-11-18
logconference.io/LOG/2023/Conference (poster)
doi.org
openreview.net