Publications related to Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

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 ...

EPFL2024

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

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen, Michele Bianco

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

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

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

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Volkan Cevher, Grigorios Chrysos, Fanghui Liu

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 ...

2024

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

Marco Picasso, Alexandre Caboussat, Maude Girardin

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

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

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 ...

2024

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

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

Pakshal Narendra Bohra

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 ...

EPFL2024

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

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 ...

EPFL Infoscience2024

Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

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Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

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

Topics in statistical physics of high-dimensional machine learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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

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

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Topics in statistical physics of high-dimensional machine learning

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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