Publications related to STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

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The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Enabling Uncertainty Estimation in Iterative Neural Networks

Pascal Fua, Nikita Durasov, Doruk Oner, Minh Hieu Lê

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

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Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

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We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Lausanne2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

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Intraday 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 ...

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Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

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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 Infoscience2024

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

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Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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EPFL2024

Sparse autoregressive neural networks for classical spin systems

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

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STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

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Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Enabling Uncertainty Estimation in Iterative Neural Networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Intraday solar irradiance forecasting using public cameras

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Sparse autoregressive neural networks for classical spin systems

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Enabling Uncertainty Estimation in Iterative Neural Networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Intraday solar irradiance forecasting using public cameras

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Sparse autoregressive neural networks for classical spin systems