Publication
Biologically plausible unsupervised learning in shallow and deep neural networks
Related publications (561)
Safe Deep Neural Networks
The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is tr ...
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 ...
EPFL2024Topics in statistical physics of high-dimensional machine learning
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 ...
EPFL2024Deep Learning Theory Through the Lens of Diagonal Linear Networks
In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of -layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...
EPFL2024Task-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 Infoscience2024Robust machine learning for neuroscientific inference
Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...
EPFL2024Deep 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 Press2024Generalization 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 ...
2024Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Yi Wu
In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...
2024Machine Learning for Modeling Stock Returns
Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...
EPFL2024