Resultaten voor 'max welling'

1. Max Welling
2. Sirui Lu
3. Lars Holdijk

Generative AI and Stochastic Thermodynamics

Max Welling is Full Professor in Machine Learning at the University of Amsterdam. He is co-founder and CTO of the startup CuspAI. Professor Welling is a member of the Dutch Royal Academy of Sciences and was the recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. Sirui Lu is Doctoral Researcher at the Max Planck Institute of Quantum Optics, Germany. His research directions are the deep integration between (quantum) physics and artificial intelligence. Previously, he obtained his bachelor's degree from the Department of Physics at Tsinghua University, Beijing. Lars Holdijk is Ph.D. student at the University of Oxford. His research focusses on the intersection of Generative Artificial Intelligence, Computational Biochemistry and Statistical Physics.

1. Max Welling
2. Sirui Lu
3. Lars Holdijk

Generative AI and Stochastic Thermodynamics

'Just as thermodynamics proved key to understanding the age of steam, stochastic thermodynamics will prove key to understanding the age of AI. This book is the first comprehensive guide to the principles of stochastic thermodynamics and how they relate to modern AI. It is much needed and will be widely read.' Neil Lawrence, University of Cambridge

1. Maurice Weiler
2. Patrick Forre
3. Erik Verlinde

Equivariant And Coordinate Independent Convolutional Networks: A Gauge Field Theory Of Neural Networks

What is the appropriate geometric structure for neural networks that process spatial signals on Euclidean spaces or more general manifolds? This question takes us on a journey which leads to a gauge field theory of convolutional networks.Feature vector fields: The spatial signals we are interested in are fields of feature vectors. Feature fields allow to describe data like images, audio, videos, point clouds, or tensor fields, such as fluid flows and electromagnetic fields.Equivariant networks commute with actions of some symmetry group on their feature spaces. The relevant group actions in this work are geometric transformations of feature fields, like translations, rotations, or reflections of images. Equivariant models generalize everything they learn over the considered group of transformations. This property makes them significantly more data efficient, interpretable, and robust in comparison to non-equivariant models.Convolutional Neural Networks (CNNs) are the most common network architecture for processing feature fields. Conventional CNNs operate on Euclidean spaces and are translation equivariant, i.e. position independent. This work explains how to extend CNNs to be equivariant under more general symmetries of space.Coordinate independence: Manifolds are in general not equipped with a canonical choice of coordinates. Feature fields and neural network layers are hence required to be coordinate independent, that is, expressible relative to different frames of reference. The ambiguity of local frames represents the gauge freedom of our neural field theory. We show that the demand for coordinate independence requires CNNs to be equivariant under local gauge transformations.To offer an easy entry, the first part of this work focuses on the representation theory of equivariant convolutional networks on Euclidean spaces. The insights gained in the Euclidean setting are subsequently leveraged to develop the full gauge theory of coordinate independent CNNs on Riemannian manifolds. In the last part, we turn to a discussion of practical applications on specific manifolds. A comprehensive literature review demonstrates the generality of our theory by showing for more than 100 models from the literature how they can be understood as specific instantiations of "Equivariant and Coordinate Independent CNNs".

1. Yue Song
2. Thomas Anderson Keller
3. Nicu Sebe

Structured Representation Learning

This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms.

1. Max Welling

Economie-lezing 2025

1. Max , Wellinghaus

Campino

Er ist nicht nur Sänger, sondern auch Schauspieler, Autor, Journalist und engagiert sich obendrein politisch - Campino, Punklegende und Frontmann der Toten Hosen, inspiriert seine Fans seit über 40 Jahren auf und neben der Bühne mit seiner unvergleichlichen Art und seinem einnehmenden Charakter. Doch was hat den Punksänger einst ins Kloster verschlagen? Wieso frühstückten er und seine Mutter jahrelang in verschiedenen Räumen? Und für wen wird Campino für immer Herr Frege sein? So vielseitig wie Campino selbst sind auch die Geschichten über den Sänger, die in diesem Buch zusammengetragen wurden: frech, unkonventionell und stets humorvoll - eben ganz so wie Campino.