Publication
A Neural Model to Predict Parameters for a Generalized Command Response Model of Intonation
Publications associées (73)
Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning
The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...
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Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...
EPFL Infoscience2024InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts
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Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...
2024Intraday solar irradiance forecasting using public cameras
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With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...
Pergamon-Elsevier Science Ltd2024An exact mapping from ReLU networks to spiking neural networks
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Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...
2023Supervised learning and inference of spiking neural networks with temporal coding
The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...
EPFL2023Task-driven neural network models predict neural dynamics of proprioception
Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi
Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...
2023From event-based surprise to lifelong learning.A journey in the timescales of adaptation
Martin Louis Lucien Rémy Barry
Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...
EPFL2023Learnable latent embeddings for joint behavioural and neural analysis
Mackenzie Mathis, Steffen Schneider, Jin Hwa Lee
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...
NATURE PORTFOLIO2023The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physic ...
Berlin2023