{"total":56,"rows":[{"id":"2312.10794","title":"A mathematical perspective on Transformers","created":"2023-12-17","cited_by_count":164,"area":null},{"id":"2107.13163","title":"Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers","created":"2021-07-28","cited_by_count":102,"area":null},{"id":"2110.13040","title":"Neural Flows: Efficient Alternative to Neural ODEs","created":"2021-10-25","cited_by_count":101,"area":null},{"id":"2206.08311","title":"Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations","created":"2022-06-16","cited_by_count":59,"area":null},{"id":"2404.11735","title":"Learning with 3D rotations, a hitchhiker's guide to SO(3)","created":"2024-04-17","cited_by_count":42,"area":null},{"id":"2203.08652","title":"Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow","created":"2022-03-16","cited_by_count":38,"area":null},{"id":"2102.07870","title":"Momentum Residual Neural Networks","created":"2021-02-15","cited_by_count":37,"area":null},{"id":"2103.06183","title":"A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations","created":"2021-03-10","cited_by_count":28,"area":null},{"id":"2103.09527","title":"Implicit Normalizing Flows","created":"2021-03-17","cited_by_count":28,"area":null},{"id":"2206.07335","title":"On Numerical Integration in Neural Ordinary Differential Equations","created":"2022-06-15","cited_by_count":21,"area":null},{"id":"2309.01213","title":"Implicit regularization of deep residual networks towards neural ODEs","created":"2023-09-03","cited_by_count":20,"area":null},{"id":"2102.01577","title":"Policy Analysis using Synthetic Controls in Continuous-Time","created":"2021-02-02","cited_by_count":18,"area":null},{"id":"2402.06578","title":"On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows","created":"2024-02-09","cited_by_count":18,"area":null},{"id":"2211.05770","title":"Efficient Image Generation with Variadic Attention Heads","created":"2022-11-10","cited_by_count":17,"area":null},{"id":"2105.08644","title":"Ab-initio study of interacting fermions at finite temperature with neural canonical transformation","created":"2021-05-18","cited_by_count":16,"area":null},{"id":"2107.07232","title":"On the expressivity of bi-Lipschitz normalizing flows","created":"2021-07-15","cited_by_count":16,"area":null},{"id":"2306.09739","title":"Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints","created":"2023-06-16","cited_by_count":15,"area":null},{"id":"2411.06311","title":"When are dynamical systems learned from time series data statistically accurate?","created":"2024-11-09","cited_by_count":15,"area":null},{"id":"2305.18460","title":"Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation","created":"2023-05-29","cited_by_count":13,"area":null},{"id":"2105.12152","title":"Density estimation on low-dimensional manifolds: an inflation-deflation approach","created":"2021-05-25","cited_by_count":11,"area":null},{"id":"2110.08649","title":"Equivariant Finite Normalizing Flows","created":"2021-10-16","cited_by_count":11,"area":null},{"id":"2210.02373","title":"Dynamical systems' based neural networks","created":"2022-10-05","cited_by_count":10,"area":null},{"id":"2102.06539","title":"Jacobian Determinant of Normalizing Flows","created":"2021-02-12","cited_by_count":8,"area":null},{"id":"2410.10253","title":"Feedback Favors the Generalization of Neural ODEs","created":"2024-10-14","cited_by_count":8,"area":null},{"id":"2506.01158","title":"Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators","created":"2025-06-01","cited_by_count":8,"area":"diffusion"},{"id":"2107.02951","title":"Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows","created":"2021-07-07","cited_by_count":7,"area":null},{"id":"2303.05323","title":"Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE","created":"2023-03-09","cited_by_count":7,"area":null},{"id":"2210.14032","title":"Whitening Convergence Rate of Coupling-based Normalizing Flows","created":"2022-10-25","cited_by_count":6,"area":null},{"id":"2402.03495","title":"Partially Stochastic Infinitely Deep Bayesian Neural Networks","created":"2024-02-05","cited_by_count":6,"area":null},{"id":"2104.13962","title":"Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics","created":"2021-04-22","cited_by_count":5,"area":null},{"id":"2311.15890","title":"Stability-Informed Initialization of Neural Ordinary Differential Equations","created":"2023-11-27","cited_by_count":5,"area":null},{"id":"2106.10911","title":"Approximation capabilities of measure-preserving neural networks","created":"2021-06-21","cited_by_count":4,"area":null},{"id":"2201.00960","title":"Neural Piecewise-Constant Delay Differential Equations","created":"2022-01-04","cited_by_count":4,"area":null},{"id":"2405.11605","title":"Switched Flow Matching: Eliminating Singularities via Switching ODEs","created":"2024-05-19","cited_by_count":4,"area":"diffusion"},{"id":"2305.12185","title":"Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?","created":"2023-05-20","cited_by_count":3,"area":null},{"id":"2305.16791","title":"On the Generalization and Approximation Capacities of Neural Controlled Differential Equations","created":"2023-05-26","cited_by_count":3,"area":null},{"id":"2312.12903","title":"A Minimal Control Family of Dynamical Systems for Universal Approximation","created":"2023-12-20","cited_by_count":3,"area":null},{"id":"2507.05164","title":"A Dynamical Systems Perspective on the Analysis of Neural Networks","created":"2025-07-07","cited_by_count":3,"area":null},{"id":"2303.12147","title":"Universal Approximation Property of Hamiltonian Deep Neural Networks","created":"2023-03-21","cited_by_count":2,"area":null},{"id":"2308.15613","title":"Mixed Variational Flows for Discrete Variables","created":"2023-08-29","cited_by_count":2,"area":null},{"id":"2401.14131","title":"Equivariant Manifold Neural ODEs and Differential Invariants","created":"2024-01-25","cited_by_count":2,"area":null},{"id":"2410.03292","title":"Demystifying the Token Dynamics of Deep Selective State Space Models","created":"2024-10-04","cited_by_count":2,"area":"ssm"},{"id":"2512.03058","title":"Dynamical Properties of Tokens in Self-Attention and Effects of Positional Encoding","created":"2025-11-25","cited_by_count":2,"area":null},{"id":"2111.13207","title":"Characteristic Neural Ordinary Differential Equations","created":"2021-11-25","cited_by_count":1,"area":null},{"id":"2205.06571","title":"Convergence Analysis of Deep Residual Networks","created":"2022-05-13","cited_by_count":1,"area":null},{"id":"2406.14265","title":"VeriFlow: Modeling Distributions for Neural Network Verification","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2511.09902","title":"Autonomous-Flow-Based Generation","created":"2025-11-13","cited_by_count":1,"area":null},{"id":"2601.06834","title":"Enhancing Low-resolution Image Representation Through Normalizing Flows","created":"2026-01-11","cited_by_count":1,"area":null},{"id":"2111.04123","title":"NeurInt : Learning to Interpolate through Neural ODEs","created":"2021-11-07","cited_by_count":0,"area":null},{"id":"2301.00007","title":"Selected aspects of complex, hypercomplex and fuzzy neural networks","created":"2022-12-29","cited_by_count":0,"area":null},{"id":"2305.17670","title":"Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning","created":"2023-05-28","cited_by_count":0,"area":null},{"id":"2602.15128","title":"PolyNODE: Variable-dimension Neural ODEs on M-polyfolds","created":"2026-02-16","cited_by_count":0,"area":null},{"id":"2603.28591","title":"Universal Approximation Constraints of Narrow ResNets: The Tunnel Effect","created":"2026-03-30","cited_by_count":0,"area":null},{"id":"2605.06172","title":"Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2607.04597","title":"Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2609.37944","title":"Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}