research
∙
05/31/2023
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, ...
share
research
∙
03/01/2023
An end-to-end SE(3)-equivariant segmentation network
Convolutional neural networks (CNNs) allow for parameter sharing and tra...
research
∙
01/31/2023
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
Understanding when the noise in stochastic gradient descent (SGD) affect...
research
∙
07/18/2022
e3nn: Euclidean Neural Networks
We present e3nn, a generalized framework for creating E(3) equivariant t...
research
∙
06/16/2021
How memory architecture affects performance and learning in simple POMDPs
Reinforcement learning is made much more complex when the agent's observ...
research
∙
06/04/2021
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Machine learning has enabled the prediction of quantum chemical properti...
research
∙
05/06/2021
Relative stability toward diffeomorphisms in deep nets indicates performance
Understanding why deep nets can classify data in large dimensions remain...
research
∙
12/30/2020
Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training
Deep learning algorithms are responsible for a technological revolution ...
research
∙
08/19/2020
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Equivariant neural networks (ENNs) are graph neural networks embedded in...
research
∙
07/22/2020
Compressing invariant manifolds in neural nets
We study how neural networks compress uninformative input space in model...
research
∙
07/04/2020
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
Curie's principle states that "when effects show certain asymmetry, this...
research
∙
06/19/2019
Disentangling feature and lazy learning in deep neural networks: an empirical study
Two distinct limits for deep learning as the net width h→∞ have been pro...
research
∙
05/26/2019
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm
How many training data are needed to learn a supervised task? It is ofte...
research
∙
01/06/2019
Scaling description of generalization with number of parameters in deep learning
We provide a description for the evolution of the generalization perform...
research
∙
11/05/2018
A General Theory of Equivariant CNNs on Homogeneous Spaces
Group equivariant convolutional neural networks (G-CNNs) have recently e...
research
∙
10/22/2018
A jamming transition from under- to over-parametrization affects loss landscape and generalization
We argue that in fully-connected networks a phase transition delimits th...
research
∙
09/25/2018
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
Deep learning has been immensely successful at a variety of tasks, rangi...
research
∙
07/06/2018
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
We present a convolutional network that is equivariant to rigid body mot...
research
∙
03/28/2018
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks)
Group equivariant and steerable convolutional neural networks (regular a...
research
∙
01/30/2018