Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA

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Learning to Reason with Neural Networks: Principles and Methods

Aryo Lotfi Jandaghi

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

EPFL2025

Asymptotics of feature learning in two-layer networks after one gradient-step

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Hugo Chao Cui, Yatin Dandi, Luca Pesce, Yue Lu

In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we model t ...

2024

A Quantitative, Digital Method to Analyze Human Figure Drawings as a Tool to Assess Body Representations Distortions in Stroke Patients

Joseph Tharayil

Objective: Human figure drawings are widely used in clinical practice as a qualitative indication of Body Representations (BRs) alterations in stroke patients. The objective of this study is to present and validate the use of a new app called QDraw for the ...

Ieee-Inst Electrical Electronics Engineers Inc2023

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Berfin Simsek

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

EPFL2023