Publications

Mitigating Biases with Diverse Ensembles and Diffusion Models

Luca Scimeca

Alexander Rubinstein

Damien Teney

Seong Joon Oh

Armand Nicolicioiu

Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut lea… (voir plus)rning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we propose an ensemble diversification framework exploiting Diffusion Probabilistic Models (DPMs) to mitigate this form of bias. We show that at particular training intervals, DPMs can generate images with novel feature combinations, even when trained on samples displaying correlated input features. We leverage this crucial property to generate synthetic counterfactuals to increase model diversity via ensemble disagreement. We show that DPM-guided diversification is sufficient to remove dependence on primary shortcut cues, without a need for additional supervised signals. We further empirically quantify its efficacy on several diversification objectives, and finally show improved generalization and diversification performance on par with prior work that relies on auxiliary data collection.

2023-11-23

ArXiv (prépublication)

arxiv.org

Propositional Logics for the Lawvere Quantale

Giorgio Bacci

Radu Mardare

Prakash Panangaden

Gordon Plotkin

2023-11-23

Electronic Notes in Theoretical Informatics and Computer Science (publié)

doi.org

arxiv.org

scSniper: Single-cell Deep Neural Network-based Identification of Prominent Biomarkers

Mingyang Li

Yanshuo Chen

Jun Ding

2023-11-23

bioRxiv (prépublication)

doi.org

Unveiling the Impact of Arsenic Toxicity on Immune Cells in Atherosclerotic Plaques: Insights from Single-Cell Multi-Omics Profiling

Kiran Makhani

Xiuhui Yang

France Dierick

Nivetha Subramaniam

Natascha Gagnon

Talin Ebrahimian

Hao Wu

Jun Ding

Koren K. Mann

2023-11-23

bioRxiv (prépublication)

doi.org

Deep Learning Model for Multi-Step Ahead Prediction of Solar Irradiance: Case of Study of Morocco

Saad Benbrahim

Loubna Benabbou

Hanane Dagdougui

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

Accurate solar irradiance forecasting is crucial for managing energy generation and consumption in the rapidly evolving landscape of renewab… (voir plus)le energy. It enables renewable energy operators to make informed decisions and maximize their output. This study employs deep learning-based forecasting models to predict the Global Horizontal Irradiance (GHI) of the R&D platform situated in Ouarzazate, Morocco. A sensitivity analysis was conducted on multiple scenarios for a one day-ahead horizon. Moreover, a forecasting technique that encompasses numerous horizons, ranging from one day to three days in advance, was evaluated. The study's findings suggest that the encoder-decoder model we proposed exhibited superior performance compared to the other models tested and produced dependable predictions.

2023-11-22

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)

doi.org

Towards an Effective Electrical Market Design: Identifying and Defining Key Criteria for Decision-Making

Souhaila Chiguer

Loubna Benabbou

Hanane Dagdougui

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

In our changing energy landscape, electricity is taking a major role in achieving decarbonization goals. Electricity can be a clean and effi… (voir plus)cient source of energy, and it is well-suited to help countries meet their climate goals. However, the electrical market is complex and constantly evolving, and it is important to carefully choose the design elements of the market to ensure that it is meeting its objectives. In this context, evaluating an electrical market's effectiveness requires a multifaceted approach that takes into account a range of elements, from environmental impact to economic viability. This paper provides an overview of several evaluation methods for different objectives to finally select the key criteria to consider in assisting decision-makers, regulators, and stakeholders in developing an electricity market that is not only effective but also reliable and sustainable.

2023-11-22

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)

doi.org

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

Max Schwarzer

Jesse Farebrother

Joshua Greaves

Ekin Dogus Cubuk

Rishabh Agarwal

Aaron Courville

Marc Gendron-Bellemare

Sergei V. Kalinin

Igor Mordatch

Pablo Samuel Castro

Kevin M Roccapriore

We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (voir plus)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.

2023-11-21

ArXiv (prépublication)

doi.org

arxiv.org

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