Publications

Towards DNA-Encoded Library Generation with GFlowNets
Michał Koziarski
Mohammed Abukalam
Vedant Shah
Louis Vaillancourt
Doris Alexandra Schuetz
Moksh Jain
Almer Van Der Sloot
Mathieu Bourgey
Anne Marinier
Yoshua Bengio
2024-03-03
GEM @ International Conference on Learning Representations (poster)
doi.org
openreview.net
Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals
Quentin Moreau
Florence Brun
Anaël Ayrolles
Jacqueline Nadel
Guillaume Dumas
Autism Spectrum Disorder (ASD) is defined by distinctive socio-cognitive behaviors that deviate from typical patterns. Notably, social imita… (voir plus)tion skills appear to be particularly impacted, manifesting early on in development. This paper compared the behavior and inter-brain dynamics of dyads made up of two typically developing (TD) participants with mixed dyads made up of ASD and TD participants during social imitation tasks. By combining kinematics and EEG-hyperscanning, we show that individuals with ASD exhibited a preference for the follower rather than the lead role in imitating scenarios. Moreover, the study revealed inter-brain synchrony differences, with low-alpha inter-brain synchrony differentiating control and mixed dyads. The study’s findings suggest the importance of studying interpersonal phenomena in dynamic and ecological settings and using hyperscanning methods to capture inter-brain dynamics during actual social interactions.
2024-03-02
Social Neuroscience (publié)
doi.org
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
Amal Rannen-Triki
Jörg Bornschein
Razvan Pascanu
Marcus Hutter
Andr'as Gyorgy
Alexandre Galashov
Yee Whye Teh
Michalis K. Titsias
We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation. While it is… (voir plus) generally known that this approach improves the overall predictive performance, especially when considering distributional shift between training and evaluation data, we here emphasize the perspective that online adaptation turns parameters into temporally changing states and provides a form of context-length extension with memory in weights, more in line with the concept of memory in neuroscience. We pay particular attention to the speed of adaptation (in terms of sample efficiency),sensitivity to the overall distributional drift, and the computational overhead for performing gradient computations and parameter updates. Our empirical study provides insights on when online adaptation is particularly interesting. We highlight that with online adaptation the conceptual distinction between in-context learning and fine tuning blurs: both are methods to condition the model on previously observed tokens.
2024-03-02
ArXiv (prépublication)
doi.org
arxiv.org
Communicating Study Design Trade-offs in Software Engineering
Martin P. Robillard
Deeksha M. Arya
Neil Ernst
Jin L.C. Guo
Maxime Lamothe
Mathieu Nassif
Nicole Novielli
Alexander Serebrenik
Igor Steinmacher
Klaas-Jan Stol
2024-03-01
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
A Compositional Typed Semantics for Universal Dependencies
Laurestine Bradford
Timothy John O'donnell
Siva Reddy
2024-03-01
ArXiv (prépublication)
doi.org
arxiv.org
Latent Idiom Recognition for a Minimalist Functional Array Language Using Equality Saturation
Jonathan Van der Cruysse
Christophe Dubach
Accelerating programs is typically done by recognizing code idioms matching high-performance libraries or hardware interfaces. However, reco… (voir plus)gnizing such idioms automatically is challenging. The idiom recognition machinery is difficult to write and requires expert knowledge. In addition, slight variations in the input program might hide the idiom and defeat the recognizer. This paper advocates for the use of a minimalist functional array language supporting a small, but expressive, set of operators. The minimalist design leads to a tiny sets of rewrite rules, which encode the language semantics. Crucially, the same minimalist language is also used to encode idioms. This removes the need for hand-crafted analysis passes, or for having to learn a complex domain-specific language to define the idioms. Coupled with equality saturation, this approach is able to match the core functions from the BLAS and PyTorch libraries on a set of computational kernels. Compared to reference C kernel implementations, the approach produces a geometric mean speedup of 1.46× for C programs using BLAS, when generating such programs from the high-level minimalist language.
2024-03-01
2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) (publié)
doi.org
arxiv.org
Learning and Aligning Structured Random Feature Networks
Vivian White
Muawiz Sajjad Chaudhary
Guy Wolf
Guillaume Lajoie
Kameron Decker Harris
Artificial neural networks (ANNs) are considered "black boxes'' due to the difficulty of interpreting their learned weights. While choosing… (voir plus) the best features is not well understood, random feature networks (RFNs) and wavelet scattering ground some ANN learning mechanisms in function space with tractable mathematics. Meanwhile, the genetic code has evolved over millions of years, shaping the brain to develop variable neural circuits with reliable structure that resemble RFNs. We explore a similar approach, embedding neuro-inspired, wavelet-like weights into multilayer RFNs. These can outperform scattering and have kernels that describe their function space at large width. We build learnable and deeper versions of these models where we can optimize separate spatial and channel covariances of the convolutional weight distributions. We find that these networks can perform comparatively with conventional ANNs while dramatically reducing the number of trainable parameters. Channel covariances are most influential, and both weight and activation alignment are needed for classification performance. Our work outlines how neuro-inspired configurations may lead to better performance in key cases and offers a potentially tractable reduced model for ANN learning.
2024-03-01
ICLR.cc/2024/Workshop/Re-Align (poster)
openreview.net
openreview.net
Quality of Service-Constrained Online Routing in High Throughput Satellites
Olivier Bélanger
Olfa Ben Yahia
Stéphane Martel
Antoine Lesage-Landry
Gunes Karabulut Kurt
High throughput satellites (HTSs) outpace traditional satellites due to their multi-beam transmission. The rise of low Earth orbit mega cons… (voir plus)tellations amplifies HTS data rate demands to terabits/second with acceptable latency. This surge in data rate necessitates multiple modems, often exceeding single device capabilities. Consequently, satellites employ several processors, forming a complex packet-switch network. This can lead to potential internal congestion and challenges in adhering to strict quality of service (QoS) constraints. While significant research exists on constellation-level routing, a literature gap remains on the internal routing within a single HTS. The intricacy of this internal network architecture presents a significant challenge to achieve high data rates. This paper introduces an online optimal flow allocation and scheduling method for HTSs. The problem is presented as a multi-commodity flow instance with different priority data streams. An initial full time horizon model is proposed as a benchmark. We apply a model predictive control (MPC) approach to enable adaptive routing based on current information and the forecast within the prediction time horizon while allowing for deviation of the latter. Importantly, MPC is inherently suited to handle uncertainty in incoming flows. Our approach minimizes the packet loss by optimally and adaptively managing the priority queue schedulers and flow exchanges between satellite processing modules. Central to our method is a routing model focusing on optimal priority scheduling to enhance data rates and maintain QoS. The model's stages are critically evaluated, and results are compared to traditional methods via numerical simulations. Through simulations, our method demonstrates performance nearly on par with the hindsight optimum, showcasing its efficiency and adaptability in addressing satellite communication challenges.
2024-03-01
2024 IEEE Aerospace Conference (publié)
doi.org
arxiv.org
ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints
Sina Kiani
Keyhan Sheshyekani
Hanane Dagdougui
This article presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed… (voir plus) hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.
2024-02-29
IEEE Transactions on Transportation Electrification (publié)
doi.org
Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities
Eslam G. Al-Sakkari
Ahmed Ragab
Hanane Dagdougui
Daria C. Boffito
Mouloud Amazouz
Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sector to mitigate cl… (voir plus)imate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. All of these developed methods will be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.
2024-02-29
Science of The Total Environment (publié)
doi.org
Data science opportunities of large language models for neuroscience and biomedicine
Danilo Bzdok
Andrew Thieme
Oleksiy Levkovskyy
Paul Wren
Thomas Ray
Siva Reddy
2024-02-29
Neuron (publié)
doi.org
Decoding face recognition abilities in the human brain
Simon Faghel-Soubeyrand
Meike Ramon
Eva Bamps
Matteo Zoia
Jessica Woodhams
Anne-Raphaelle Richoz
Roberto Caldara
Frédéric Gosselin
Ian Charest
Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusiv… (voir plus)e. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognisers—and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared computations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain computations of super-recognisers and mid-level computations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and computations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. The ability to robustly recognise faces is crucial to our success as social beings. Yet, we still know little about the brain mechanisms allowing some individuals to excel at face recognition. This study builds on a sizeable neural dataset measuring the brain activity of individuals with extraordinary face recognition abilities—super-recognisers—to tackle this challenge. Using state-of-the-art computational methods, we show robust prediction of face recognition abilities in single individuals from a mere second of brain activity, and revealed specific brain computations supporting individual differences in face recognition ability. Doing so, we provide direct empirical evidence for an association between semantic computations and face recognition abilities in the human brain—a key component of prominent face recognition models.
2024-02-29
PNAS Nexus (publié)
doi.org