Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis

24 October 2022

Luca Galimberti

Papers citing "Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis"

49 / 49 papers shown

Title
Deep Kalman Filters Can Filter Blanka Hovart Anastasis Kratsios Yannick Limmer Xuwei Yang 78 1 0 31 Dec 2024
Neural Operators Can Play Dynamic Stackelberg Games Guillermo Alvarez Ibrahim Ekren Anastasis Kratsios Xuwei Yang 54 0 0 14 Nov 2024
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective Samuel Lanthaler 69 3 0 26 Jun 2024
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach Reza Arabpour John Armstrong Luca Galimberti Anastasis Kratsios Giulia Livieri 18 2 0 30 May 2024
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning Anastasis Kratsios Takashi Furuya Jose Antonio Lara Benitez Matti Lassas Maarten V. de Hoop 60 13 0 13 Apr 2024
Operator Learning: Algorithms and Analysis Nikola B. Kovachki S. Lanthaler Andrew M. Stuart 97 30 0 24 Feb 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts Anastasis Kratsios Haitz Sáez de Ocáriz Borde Takashi Furuya Marc T. Law MoE 93 1 0 05 Feb 2024
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum Tin Sum Cheng Aurelien Lucchi Anastasis Kratsios David Belius 52 8 0 02 Feb 2024
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries Haitz Sáez de Ocáriz Borde Anastasis Kratsios 110 4 0 23 Oct 2023
Memory of recurrent networks: Do we compute it right? Giovanni Ballarin Lyudmila Grigoryeva Juan-Pablo Ortega 37 4 0 02 May 2023
Infinite-dimensional reservoir computing Lukas Gonon Lyudmila Grigoryeva Juan-Pablo Ortega 59 9 0 02 Apr 2023
Operator learning with PCA-Net: upper and lower complexity bounds S. Lanthaler 36 25 0 28 Mar 2023
A Brief Survey on the Approximation Theory for Sequence Modelling Hao Jiang Qianxiao Li Zhong Li Shida Wang AI4TS 44 12 0 27 Feb 2023
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation Jose Antonio Lara Benitez Takashi Furuya F. Faucher Anastasis Kratsios X. Tricoche Maarten V. de Hoop 55 18 0 27 Jan 2023
Limitations on approximation by deep and shallow neural networks G. Petrova P. Wojtaszczyk 89 7 0 30 Nov 2022
Chaotic Hedging with Iterated Integrals and Neural Networks Ariel Neufeld Philipp Schmocker 70 10 0 21 Sep 2022
Small Transformers Compute Universal Metric Embeddings Anastasis Kratsios Valentin Debarnot Ivan Dokmanić 75 11 0 14 Sep 2022
Wavelet neural operator: a neural operator for parametric partial differential equations Tapas Tripura S. Chakraborty 43 63 0 04 May 2022
Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions? Anastasis Kratsios Behnoosh Zamanlooy MLT 91 3 0 24 Apr 2022
Deep Learning for the Benes Filter Alexander Lobbe 38 3 0 09 Mar 2022
Pricing options on flow forwards by neural networks in Hilbert space F. Benth Nils Detering Luca Galimberti 44 7 0 17 Feb 2022
Computation of conditional expectations with guarantees Patrick Cheridito Balint Gersey 33 2 0 03 Dec 2021
On the Provable Generalization of Recurrent Neural Networks Lifu Wang Bo Shen Bo Hu Xing Cao 70 8 0 29 Sep 2021
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S. Alberti Ernesto De Vito Matti Lassas Luca Ratti Matteo Santacesaria 39 30 0 11 Jun 2021
Metric Entropy Limits on Recurrent Neural Network Learning of Linear Dynamical Systems Clemens Hutter R. Gül Helmut Bölcskei 38 9 0 06 May 2021
Two-layer neural networks with values in a Banach space Yury Korolev 48 24 0 05 May 2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 128 115 0 28 Feb 2021
Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems C. Beck S. Becker Patrick Cheridito Arnulf Jentzen Ariel Neufeld 32 11 0 02 Dec 2020
Fading memory echo state networks are universal Lukas Gonon Juan-Pablo Ortega 46 59 0 22 Oct 2020
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 411 2,355 0 18 Oct 2020
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering Calypso Herrera Florian Krach Josef Teichmann BDL AI4TS 28 31 0 08 Jun 2020
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography T. Bubba Mathilde Galinier Matti Lassas M. Prato Luca Ratti S. Siltanen 37 29 0 02 Jun 2020
Deep Network Approximation for Smooth Functions Jianfeng Lu Zuowei Shen Haizhao Yang Shijun Zhang 89 247 0 09 Jan 2020
Deep learning architectures for nonlinear operator functions and nonlinear inverse problems Maarten V. de Hoop Matti Lassas C. Wong 36 26 0 23 Dec 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 161 2,082 0 08 Oct 2019
Federated Learning: Challenges, Methods, and Future Directions Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 83 4,470 0 21 Aug 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 73 631 0 14 Aug 2019
Continual learning with hypernetworks J. Oswald Christian Henning Benjamin Grewe João Sacramento CLL 50 354 0 03 Jun 2019
Universal Approximation with Deep Narrow Networks Patrick Kidger Terry Lyons 63 329 0 21 May 2019
Deep Neural Network Approximation Theory Dennis Elbrächter Dmytro Perekrestenko Philipp Grohs Helmut Bölcskei 35 210 0 08 Jan 2019
Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography T. Bubba Gitta Kutyniok Matti Lassas M. März Wojciech Samek S. Siltanen Vignesh Srinivasan 126 136 0 12 Nov 2018
Graph HyperNetworks for Neural Architecture Search Chris Zhang Mengye Ren R. Urtasun GNN 38 276 0 12 Oct 2018
Reservoir Computing Universality With Stochastic Inputs Lukas Gonon Juan-Pablo Ortega 27 111 0 07 Jul 2018
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 453 129,831 0 12 Jun 2017
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks Peter L. Bartlett Nick Harvey Christopher Liaw Abbas Mehrabian 142 427 0 08 Mar 2017
Error bounds for approximations with deep ReLU networks Dmitry Yarotsky 137 1,226 0 03 Oct 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.4K 192,638 0 10 Dec 2015
Deep Kalman Filters Rahul G. Krishnan Uri Shalit David Sontag BDL AI4TS 56 372 0 16 Nov 2015
On the difficulty of training Recurrent Neural Networks Razvan Pascanu Tomas Mikolov Yoshua Bengio ODL 134 5,318 0 21 Nov 2012