{"total":200,"rows":[{"id":"2102.03657","title":"Neural SDEs as Infinite-Dimensional GANs","created":"2021-02-06","cited_by_count":161,"area":null},{"id":"2201.12220","title":"Neural Optimal Transport","created":"2022-01-28","cited_by_count":126,"area":null},{"id":"2103.12803","title":"JFB: Jacobian-Free Backpropagation for Implicit Networks","created":"2021-03-23","cited_by_count":86,"area":null},{"id":"2105.13493","title":"Efficient and Accurate Gradients for Neural SDEs","created":"2021-05-27","cited_by_count":71,"area":null},{"id":"2406.07456","title":"fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions","created":"2024-06-11","cited_by_count":64,"area":null},{"id":"2211.06291","title":"Do Bayesian Neural Networks Need To Be Fully Stochastic?","created":"2022-11-11","cited_by_count":63,"area":null},{"id":"2112.02424","title":"Variational Wasserstein gradient flow","created":"2021-12-04","cited_by_count":53,"area":null},{"id":"2101.08366","title":"Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs","created":"2021-01-20","cited_by_count":43,"area":null},{"id":"2101.05390","title":"Universal Approximation Theorems for Differentiable Geometric Deep Learning","created":"2021-01-13","cited_by_count":41,"area":null},{"id":"2106.04546","title":"LEADS: Learning Dynamical Systems that Generalize Across Environments","created":"2021-06-08","cited_by_count":40,"area":null},{"id":"2110.03303","title":"Universal Approximation Under Constraints is Possible with Transformers","created":"2021-10-07","cited_by_count":32,"area":null},{"id":"2205.11775","title":"Constrained Monotonic Neural Networks","created":"2022-05-24","cited_by_count":32,"area":null},{"id":"2210.04345","title":"LieGG: Studying Learned Lie Group Generators","created":"2022-10-09","cited_by_count":31,"area":null},{"id":"2410.10807","title":"HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees","created":"2024-10-14","cited_by_count":31,"area":null},{"id":"2403.02467","title":"Applied Causal Inference Powered by ML and AI","created":"2024-03-04","cited_by_count":30,"area":null},{"id":"2206.09527","title":"Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations","created":"2022-06-20","cited_by_count":29,"area":null},{"id":"2505.22483","title":"A Closer Look at Multimodal Representation Collapse","created":"2025-05-28","cited_by_count":28,"area":null},{"id":"2209.11395","title":"Achieve the Minimum Width of Neural Networks for Universal Approximation","created":"2022-09-23","cited_by_count":26,"area":null},{"id":"2406.12839","title":"Evaluating the design space of diffusion-based generative models","created":"2024-06-18","cited_by_count":26,"area":"diffusion"},{"id":"2101.06317","title":"Machine-Learning Mathematical Structures","created":"2021-01-15","cited_by_count":25,"area":null},{"id":"2201.13094","title":"Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer","created":"2022-01-31","cited_by_count":22,"area":null},{"id":"2210.13300","title":"Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis","created":"2022-10-24","cited_by_count":22,"area":null},{"id":"2401.02731","title":"Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks","created":"2024-01-05","cited_by_count":22,"area":"moe"},{"id":"2401.07187","title":"A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models","created":"2024-01-14","cited_by_count":20,"area":null},{"id":"2202.10847","title":"UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography","created":"2022-02-22","cited_by_count":19,"area":null},{"id":"2410.09375","title":"Looped ReLU MLPs May Be All You Need as Practical Programmable Computers","created":"2024-10-12","cited_by_count":19,"area":null},{"id":"2111.05841","title":"Physics-enhanced deep surrogates for partial differential equations","created":"2021-11-10","cited_by_count":18,"area":null},{"id":"2309.10402","title":"Minimum width for universal approximation using ReLU networks on compact domain","created":"2023-09-19","cited_by_count":18,"area":null},{"id":"2402.06323","title":"How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers","created":"2024-02-09","cited_by_count":18,"area":null},{"id":"2502.06018","title":"Kolmogorov-Arnold Fourier Networks","created":"2025-02-09","cited_by_count":18,"area":null},{"id":"2502.06283","title":"On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth","created":"2025-02-10","cited_by_count":18,"area":null},{"id":"2307.12904","title":"Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs","created":"2023-07-24","cited_by_count":16,"area":null},{"id":"2402.01052","title":"Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation","created":"2024-02-01","cited_by_count":14,"area":null},{"id":"2408.04569","title":"Activation degree thresholds and expressiveness of polynomial neural networks","created":"2024-08-08","cited_by_count":14,"area":null},{"id":"2109.11354","title":"Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks","created":"2021-09-23","cited_by_count":12,"area":null},{"id":"2206.04360","title":"A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks","created":"2022-06-09","cited_by_count":12,"area":null},{"id":"2209.06788","title":"Small Transformers Compute Universal Metric Embeddings","created":"2022-09-14","cited_by_count":12,"area":null},{"id":"2211.13866","title":"Minimal Width for Universal Property of Deep RNN","created":"2022-11-25","cited_by_count":12,"area":null},{"id":"2306.12548","title":"Finite-time Lyapunov exponents of deep neural networks","created":"2023-06-21","cited_by_count":12,"area":null},{"id":"2310.01105","title":"Energy-Guided Continuous Entropic Barycenter Estimation for General Costs","created":"2023-10-02","cited_by_count":12,"area":null},{"id":"2201.09656","title":"A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations","created":"2021-12-17","cited_by_count":11,"area":null},{"id":"2302.06960","title":"Data pruning and neural scaling laws: fundamental limitations of score-based algorithms","created":"2023-02-14","cited_by_count":11,"area":null},{"id":"2303.08085","title":"Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations","created":"2023-03-14","cited_by_count":11,"area":null},{"id":"2412.05994","title":"PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations","created":"2024-12-08","cited_by_count":11,"area":null},{"id":"2502.09844","title":"Solving Empirical Bayes via Transformers","created":"2025-02-14","cited_by_count":11,"area":null},{"id":"2603.11487","title":"Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks","created":"2026-03-12","cited_by_count":11,"area":null},{"id":"2202.03841","title":"Width is Less Important than Depth in ReLU Neural Networks","created":"2022-02-08","cited_by_count":10,"area":null},{"id":"2404.03176","title":"Information-Theoretic Generalization Bounds for Deep Neural Networks","created":"2024-04-04","cited_by_count":10,"area":null},{"id":"2411.03884","title":"Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models","created":"2024-11-06","cited_by_count":9,"area":null},{"id":"2302.10524","title":"LU-Net: Invertible Neural Networks Based on Matrix Factorization","created":"2023-02-21","cited_by_count":8,"area":null},{"id":"2403.06342","title":"Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation","created":"2024-03-10","cited_by_count":8,"area":null},{"id":"2505.09022","title":"Block-Biased Mamba for Long-Range Sequence Processing","created":"2025-05-13","cited_by_count":8,"area":"ssm"},{"id":"2107.02550","title":"Universal approximation and model compression for radial neural networks","created":"2021-07-06","cited_by_count":7,"area":null},{"id":"2111.04682","title":"SMU: smooth activation function for deep networks using smoothing maximum technique","created":"2021-11-08","cited_by_count":7,"area":null},{"id":"2308.03812","title":"Noncompact uniform universal approximation","created":"2023-08-07","cited_by_count":7,"area":null},{"id":"2401.03965","title":"Differential Equations for Continuous-Time Deep Learning","created":"2024-01-08","cited_by_count":7,"area":null},{"id":"2410.14759","title":"Universal approximation results for neural networks with non-polynomial activation function over non-compact domains","created":"2024-10-18","cited_by_count":7,"area":null},{"id":"2103.11435","title":"A deep learning approach to data-driven model-free pricing and to martingale optimal transport","created":"2021-03-21","cited_by_count":6,"area":null},{"id":"2108.06711","title":"Towards Understanding Theoretical Advantages of Complex-Reaction Networks","created":"2021-08-15","cited_by_count":6,"area":null},{"id":"2112.08091","title":"Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems","created":"2021-12-15","cited_by_count":6,"area":null},{"id":"2204.11231","title":"Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?","created":"2022-04-24","cited_by_count":6,"area":null},{"id":"2212.00802","title":"An Introduction to Kernel and Operator Learning Methods for Homogenization by Self-consistent Clustering Analysis","created":"2022-12-01","cited_by_count":6,"area":null},{"id":"2305.12205","title":"Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions","created":"2023-05-20","cited_by_count":6,"area":null},{"id":"2305.15901","title":"Consistent Optimal Transport with Empirical Conditional Measures","created":"2023-05-25","cited_by_count":6,"area":null},{"id":"2105.03336","title":"Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs","created":"2021-05-07","cited_by_count":5,"area":null},{"id":"2109.04386","title":"ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions","created":"2021-09-09","cited_by_count":5,"area":null},{"id":"2211.00335","title":"Recurrent Neural Networks and Universal Approximation of Bayesian Filters","created":"2022-11-01","cited_by_count":5,"area":null},{"id":"2305.13508","title":"DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation","created":"2023-05-22","cited_by_count":5,"area":null},{"id":"2404.04549","title":"Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders","created":"2024-04-06","cited_by_count":5,"area":null},{"id":"2411.09644","title":"Neural Operators Can Play Dynamic Stackelberg Games","created":"2024-11-14","cited_by_count":5,"area":null},{"id":"2502.04274","title":"Orthogonal Representation Learning for Estimating Causal Quantities","created":"2025-02-06","cited_by_count":5,"area":null},{"id":"2602.12368","title":"A Machine Learning Approach to the Nirenberg Problem","created":"2026-02-12","cited_by_count":5,"area":null},{"id":"2105.07743","title":"Universal Regular Conditional Distributions","created":"2021-05-17","cited_by_count":4,"area":null},{"id":"2106.09693","title":"Orthogonal-Pad\\'e Activation Functions: Trainable Activation functions for smooth and faster convergence in deep networks","created":"2021-06-17","cited_by_count":4,"area":null},{"id":"2110.08996","title":"Finding Everything within Random Binary Networks","created":"2021-10-18","cited_by_count":4,"area":null},{"id":"2201.06298","title":"Parameterized Convex Universal Approximators for Decision-Making Problems","created":"2022-01-17","cited_by_count":4,"area":null},{"id":"2203.03179","title":"Detecting data-driven robust statistical arbitrage strategies with deep neural networks","created":"2022-03-07","cited_by_count":4,"area":null},{"id":"2209.10909","title":"Vanilla Feedforward Neural Networks as a Discretization of Dynamical Systems","created":"2022-09-22","cited_by_count":4,"area":null},{"id":"2301.00942","title":"Deep Learning and Computational Physics (Lecture Notes)","created":"2023-01-03","cited_by_count":4,"area":null},{"id":"2304.10552","title":"Approximation and interpolation of deep neural networks","created":"2023-04-20","cited_by_count":4,"area":null},{"id":"2310.19603","title":"Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals","created":"2023-10-30","cited_by_count":4,"area":null},{"id":"2408.09624","title":"Attention is a smoothed cubic spline","created":"2024-08-19","cited_by_count":4,"area":null},{"id":"2409.00297","title":"On Expressive Power of Quantized Neural Networks under Fixed-Point Arithmetic","created":"2024-08-30","cited_by_count":4,"area":null},{"id":"2411.00835","title":"Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning","created":"2024-10-29","cited_by_count":4,"area":null},{"id":"2504.15110","title":"Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections","created":"2025-04-21","cited_by_count":4,"area":null},{"id":"2510.13025","title":"Information Shapes Koopman Representation","created":"2025-10-14","cited_by_count":4,"area":null},{"id":"2111.09705","title":"Learning Free-Surface Flow with Physics-Informed Neural Networks","created":"2021-11-17","cited_by_count":3,"area":null},{"id":"2211.08900","title":"Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs","created":"2022-11-16","cited_by_count":3,"area":null},{"id":"2301.13370","title":"On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters","created":"2023-01-31","cited_by_count":3,"area":null},{"id":"2303.06389","title":"Uncertainty-Aware Instance Reweighting for Off-Policy Learning","created":"2023-03-11","cited_by_count":3,"area":null},{"id":"2305.00044","title":"Hedonic Prices and Quality Adjusted Price Indices Powered by AI","created":"2023-04-28","cited_by_count":3,"area":null},{"id":"2305.16910","title":"Universal approximation with complex-valued deep narrow neural networks","created":"2023-05-26","cited_by_count":3,"area":null},{"id":"2310.10126","title":"A Non-monotonic Smooth Activation Function","created":"2023-10-16","cited_by_count":3,"area":null},{"id":"2404.17868","title":"Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs","created":"2024-04-27","cited_by_count":3,"area":null},{"id":"2406.09310","title":"Neural networks in non-metric spaces","created":"2024-06-13","cited_by_count":3,"area":null},{"id":"2504.07371","title":"Minimum width for universal approximation using squashable activation functions","created":"2025-04-10","cited_by_count":3,"area":null},{"id":"2505.11589","title":"A Training Framework for Optimal and Stable Training of Polynomial Neural Networks","created":"2025-05-16","cited_by_count":3,"area":null},{"id":"2507.04898","title":"When do World Models Successfully Learn Dynamical Systems?","created":"2025-07-07","cited_by_count":3,"area":"wm"},{"id":"2510.15651","title":"Deep Neural ODE Operator Networks for PDEs","created":"2025-10-17","cited_by_count":3,"area":null},{"id":"2105.07228","title":"Analysis of Structured Deep Kernel Networks","created":"2021-05-15","cited_by_count":2,"area":null}],"next_cursor":"100"}