p7math.DS

math.DS

50 papers

Theoretical Foundations of Deep Selective State-Space Models

Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker, Cristopher Salvi, Terry Lyons

2402.19047

A Mean Field Theory of Batch Normalization

Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz

1902.08129

On the relation between Gaussian process quadratures and sigma-point methods

Simo Särkkä, Jouni Hartikainen, Lennart Svensson, Fredrik Sandblom

1504.05994

UnICORNN: A recurrent model for learning very long time dependencies

T. Konstantin Rusch, Siddhartha Mishra

2103.05487

Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization

Anas Barakat, Pascal Bianchi

1810.02263

A general system of differential equations to model first order adaptive algorithms

André Belotto da Silva, Maxime Gazeau

1810.13108

Funnel Libraries for Real-Time Robust Feedback Motion Planning

Anirudha Majumdar, Russ Tedrake

1601.04037

Lipschitz Recurrent Neural Networks

N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney

2006.12070

Forecasting Sequential Data using Consistent Koopman Autoencoders

Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney

2003.02236

Lagrangian Neural Networks

Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, Shirley Ho

2003.04630

Mean Field Residual Networks: On the Edge of Chaos

Greg Yang, Samuel S. Schoenholz

1712.08969

Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent

James P. Bailey, Gauthier Gidel, Georgios Piliouras

1907.04392

Vortices Instead of Equilibria in MinMax Optimization: Chaos and Butterfly Effects of Online Learning in Zero-Sum Games

Yun Kuen Cheung, Georgios Piliouras

1905.08396

Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability

Shaowu Pan, Karthik Duraisamy

1906.03663

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

Maziar Raissi, Paris Perdikaris, George Em Karniadakis

1801.01236

Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Maziar Raissi, Paris Perdikaris, George Em Karniadakis

1711.10561

Deep learning for universal linear embeddings of nonlinear dynamics

Bethany Lusch, J. Nathan Kutz, Steven L. Brunton

1712.09707

On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems

Shaowu Pan, Karthik Duraisamy

1902.05198

Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations

Maziar Raissi, Paris Perdikaris, George Em Karniadakis

1703.10230

The Adjoint Petrov-Galerkin Method for Non-Linear Model Reduction

Eric J. Parish, Christopher Wentland, Karthik Duraisamy

1810.03455

Limits of local-global convergent graph sequences

Hamed Hatami, László Lovász, Balázs Szegedy

1205.4356

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi

1710.04340

Universal Differential Equations for Scientific Machine Learning

Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, Kirill Zubov, Rohit Supekar, Dominic Skinner, Ali Ramadhan, Alan Edelman

2001.04385

Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions

Ioannis Panageas, Georgios Piliouras

1605.00405

Neural signature kernels as infinite-width-depth-limits of controlled ResNets

Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi

2303.17671

Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem

Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang

1912.04378

Exact Recovery of Chaotic Systems from Highly Corrupted Data

Giang Tran, Rachel Ward

1607.01067

Variational approach for learning Markov processes from time series data

Hao Wu, Frank Noé

1707.04659

Inferring biological networks by sparse identification of nonlinear dynamics

Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz

1605.08368

Family of chaotic maps from game theory

Thiparat Chotibut, Fryderyk Falniowski, Michal Misiurewicz, Georgios Piliouras

1807.06831

Independent Learning in Stochastic Games

Asuman Ozdaglar, Muhammed O. Sayin, Kaiqing Zhang

2111.11743

Learning Stable Deep Dynamics Models

Gaurav Manek, J. Zico Kolter

2001.06116

Chaos as an Intermittently Forced Linear System

Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor, Eurika Kaiser, J. Nathan Kutz

1608.05306

Efficient and Accurate Gradients for Neural SDEs

Patrick Kidger, James Foster, Xuechen Li, Terry Lyons

2105.13493

Flow map matching with stochastic interpolants: A mathematical framework for consistency models

Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden

2406.07507

Incremental Stochastic Subgradient Algorithms for Convex Optimization

S Sundhar Ram, A Nedich, V. V. Veeravalli

0806.1092

Decentralized Q-Learning in Zero-sum Markov Games

Muhammed O. Sayin, Kaiqing Zhang, David S. Leslie, Tamer Basar, Asuman Ozdaglar

2106.02748

Optimization Techniques on Riemannian Manifolds

Steven Thomas Smith

1407.5965

Neural graphical modelling in continuous-time: consistency guarantees and algorithms

Alexis Bellot, Kim Branson, Mihaela van der Schaar

2105.02522

Neural Rough Differential Equations for Long Time Series

James Morrill, Cristopher Salvi, Patrick Kidger, James Foster, Terry Lyons

2009.08295

On a conjecture of Sokal concerning roots of the independence polynomial

Han Peters, Guus Regts

1701.08049

Perfect Matchings as IID Factors on Non-Amenable Groups

Russell Lyons, Fedor Nazarov

0911.0092

Gradient Starvation: A Learning Proclivity in Neural Networks

Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, Guillaume Lajoie

2011.09468

Fast and Deep Graph Neural Networks

Claudio Gallicchio, Alessio Micheli

1911.08941

Fictitious play in zero-sum stochastic games

Muhammed O. Sayin, Francesca Parise, Asuman Ozdaglar

2010.04223

Geometric Optimization Methods for Adaptive Filtering

Steven Thomas Smith

1305.1886

An inverse theorem for the Gowers U^{s+1}[N]-norm (announcement)

Ben Green, Terence Tao, Tamar Ziegler

1006.0205

An inverse theorem for the uniformity seminorms associated with the action of $F^ω$

Vitaly Bergelson, Terence Tao, Tamar Ziegler

0901.2602

Nilsequences and a structure theorem for topological dynamical systems

Bernard Host, Bryna Kra, Alejandro Maass

0905.3098

The inverse conjecture for the Gowers norm over finite fields via the correspondence principle

Terence Tao, Tamar Ziegler

0810.5527