Publications

scSniper: Single-cell Deep Neural Network-based Identification of Prominent Biomarkers
Mingyang Li
Yanshuo Chen
Jun Ding
2023-11-22
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
2023-11-22
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.
2023-11-21
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.
2023-11-21
2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (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 A. 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.
2023-11-20
Journal of Neuroscience (publié)
doi.org
A Study of Human-Robot Handover through Human-Human Object Transfer
Charlotte Morissette
Bobak H. Baghi
Francois 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.
2023-11-20
ArXiv (prépublication)
doi.org
arxiv.org
Challenging Common Assumptions About Catastrophic Forgetting and Knowledge Accumulation
Timothee LESORT
Oleksiy Ostapenko
Pau Rodríguez
Diganta Misra
Md Rifat Arefin
Laurent Charlin
Irina Rish
Building learning agents that can progressively learn and accumulate knowledge is the core goal of the continual learning (CL) research fiel… (voir plus)d. Unfortunately, training a model on new data usually compromises the performance on past data. In the CL literature, this effect is referred to as catastrophic forgetting (CF). CF has been largely studied, and a plethora of methods have been proposed to address it on short sequences of non-overlapping tasks. In such setups, CF always leads to a quick and significant drop in performance in past tasks. Nevertheless, despite CF, recent work showed that SGD training on linear models accumulates knowledge in a CL regression setup. This phenomenon becomes especially visible when tasks reoccur. We might then wonder if DNNs trained with SGD or any standard gradient-based optimization accumulate knowledge in such a way. Such phenomena would have interesting consequences for applying DNNs to real continual scenarios. Indeed, standard gradient-based optimization methods are significantly less computationally expensive than existing CL algorithms. In this paper, we study the progressive knowledge accumulation (KA) in DNNs trained with gradient-based algorithms in long sequences of tasks with data re-occurrence. We propose a new framework, SCoLe (Scaling Continual Learning), to investigate KA and discover that catastrophic forgetting has a limited effect on DNNs trained with SGD. When trained on long sequences with data sparsely re-occurring, the overall accuracy improves, which might be counter-intuitive given the CF phenomenon. We empirically investigate KA in DNNs under various data occurrence frequencies and propose simple and scalable strategies to increase knowledge accumulation in DNNs.
2023-11-19
Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Continually learning representations at scale
Alexandre Galashov
Jovana Mitrovic
Dhruva Tirumala
Yee Whye Teh
Timothy Nguyen
Arslan Chaudhry
Razvan Pascanu
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
proceedings.mlr.press
proceedings.mlr.press
Dealing with Non-Stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar
Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a versatile learning framework, particularly in scenarios wh… (voir plus)ere centralized training is either not possible or not practical. One of the critical challenges in decentralized deep MARL is the non-stationarity of the learning environment when multiple agents are learning concurrently. A commonly used and efficient scheme for decentralized MARL is independent learning in which agents concurrently update their policies independently of each other. We first show that independent learning does not always converge, while sequential learning where agents update their policies one after another in a sequence is guaranteed to converge to an agent-by-agent optimal solution. In sequential learning, when one agent updates its policy, all other agent's policies are kept fixed, alleviating the challenge of non-stationarity due to simultaneous updates in other agents' policies. However, it can be slow because only one agent is learning at any time. Therefore it might also not always be practical. In this work, we propose a decentralized cooperative MARL algorithm based on multi-timescale learning. In multi-timescale learning, all agents learn simultaneously, but at different learning rates. In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate. This speeds up sequential learning, while also minimizing non-stationarity caused by other agents updating concurrently. Multi-timescale learning outperforms state-of-the-art decentralized learning methods on a set of challenging multi-agent cooperative tasks in the epymarl(Papoudakis et al., 2020) benchmark. This can be seen as a first step towards more general decentralized cooperative deep MARL methods based on multi-timescale learning.
2023-11-19
Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning
Safa Alver
Doina Precup
Learning models of the environment from pure interaction is often considered an essential component of building lifelong reinforcement learn… (voir plus)ing agents. However, the common practice in model-based reinforcement learning is to learn models that model every aspect of the agent’s environment, regardless of whether they are important in coming up with optimal decisions or not. In this paper, we argue that such models are not particularly well-suited for performing scalable and robust planning in lifelong reinforcement learning scenarios and we propose new kinds of models that only model the relevant aspects of the environment, which we call \emph{minimal value-equivalent partial models}. After providing a formal definition for these models, we provide theoretical results demonstrating the scalability advantages of performing planning with such models and then perform experiments to empirically illustrate our theoretical results. Then, we provide some useful heuristics on how to learn these kinds of models with deep learning architectures and empirically demonstrate that models learned in such a way can allow for performing planning that is robust to distribution shifts and compounding model errors. Overall, both our theoretical and empirical results suggest that minimal value-equivalent partial models can provide significant benefits to performing scalable and robust planning in lifelong reinforcement learning scenarios.
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
Ali Rahimi-Kalahroudi
Janarthanan Rajendran
Ida Momennejad
Harm van Seijen
Sarath Chandar
One of the key behavioral characteristics used in neuroscience to determine whether the subject of study -- be it a rodent or a human -- exh… (voir plus)ibits model-based learning is effective adaptation to local changes in the environment, a particular form of adaptivity that is the focus of this work. In reinforcement learning, however, recent work has shown that modern deep model-based reinforcement-learning (MBRL) methods adapt poorly to local environment changes. An explanation for this mismatch is that MBRL methods are typically designed with sample-efficiency on a single task in mind and the requirements for effective adaptation are substantially higher, both in terms of the learned world model and the planning routine. One particularly challenging requirement is that the learned world model has to be sufficiently accurate throughout relevant parts of the state-space. This is challenging for deep-learning-based world models due to catastrophic forgetting. And while a replay buffer can mitigate the effects of catastrophic forgetting, the traditional first-in-first-out replay buffer precludes effective adaptation due to maintaining stale data. In this work, we show that a conceptually simple variation of this traditional replay buffer is able to overcome this limitation. By removing only samples from the buffer from the local neighbourhood of the newly observed samples, deep world models can be built that maintain their accuracy across the state-space, while also being able to effectively adapt to local changes in the reward function. We demonstrate this by applying our replay-buffer variation to a deep version of the classical Dyna method, as well as to recent methods such as PlaNet and DreamerV2, demonstrating that deep model-based methods can adapt effectively as well to local changes in the environment.
2023-11-19
Conference on Lifelong Learning Agents (publié)
doi.org
proceedings.mlr.press
Responsible AI Research Needs Impact Statements Too
A.R. 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.
2023-11-19
ArXiv (prépublication)
doi.org
arxiv.org