{"total":75,"rows":[{"id":"2302.01178","title":"Convolutional Neural Operators for robust and accurate learning of PDEs","created":"2023-02-02","cited_by_count":214,"area":null},{"id":"2109.14545","title":"Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark","created":"2021-09-29","cited_by_count":155,"area":null},{"id":"2312.14688","title":"A Mathematical Guide to Operator Learning","created":"2023-12-22","cited_by_count":76,"area":null},{"id":"2409.10594","title":"Kolmogorov-Arnold Transformer","created":"2024-09-16","cited_by_count":56,"area":null},{"id":"2101.08286","title":"Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem","created":"2021-01-20","cited_by_count":53,"area":null},{"id":"2105.00266","title":"Data-driven discovery of Green's functions with human-understandable deep learning","created":"2021-05-01","cited_by_count":44,"area":null},{"id":"2406.14495","title":"rKAN: Rational Kolmogorov-Arnold Networks","created":"2024-06-20","cited_by_count":35,"area":null},{"id":"2102.00491","title":"Learning elliptic partial differential equations with randomized linear algebra","created":"2021-01-31","cited_by_count":29,"area":null},{"id":"2204.12789","title":"Learning Green's functions associated with time-dependent partial differential equations","created":"2022-04-27","cited_by_count":26,"area":null},{"id":"2111.00998","title":"PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning","created":"2021-11-01","cited_by_count":24,"area":null},{"id":"2102.09407","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","created":"2021-02-18","cited_by_count":23,"area":null},{"id":"2503.13165","title":"From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective","created":"2025-03-17","cited_by_count":23,"area":null},{"id":"2402.00949","title":"Geometry of Polynomial Neural Networks","created":"2024-02-01","cited_by_count":21,"area":null},{"id":"2501.18915","title":"Algebra Unveils Deep Learning -- An Invitation to Neuroalgebraic Geometry","created":"2025-01-31","cited_by_count":21,"area":null},{"id":"2212.04971","title":"PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data","created":"2022-12-09","cited_by_count":20,"area":null},{"id":"2101.04844","title":"Reproducing Activation Function for Deep Learning","created":"2021-01-13","cited_by_count":19,"area":null},{"id":"2107.10234","title":"Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks","created":"2021-07-21","cited_by_count":19,"area":null},{"id":"2310.04867","title":"Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks","created":"2023-10-07","cited_by_count":18,"area":null},{"id":"2208.03322","title":"Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion","created":"2022-08-05","cited_by_count":16,"area":null},{"id":"2309.04699","title":"Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data","created":"2023-09-09","cited_by_count":16,"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":"2205.01549","title":"Adaptable Adapters","created":"2022-05-03","cited_by_count":13,"area":null},{"id":"2302.01470","title":"Learning to Optimize for Reinforcement Learning","created":"2023-02-03","cited_by_count":12,"area":null},{"id":"2410.00722","title":"On the Geometry and Optimization of Polynomial Convolutional Networks","created":"2024-10-01","cited_by_count":11,"area":null},{"id":"2208.03680","title":"On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver","created":"2022-08-07","cited_by_count":9,"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":"2505.11846","title":"Learning on a Razor's Edge: Identifiability and Singularity of Polynomial Neural Networks","created":"2025-05-17","cited_by_count":9,"area":null},{"id":"2311.05116","title":"Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications","created":"2023-11-09","cited_by_count":8,"area":null},{"id":"2401.17739","title":"Operator learning without the adjoint","created":"2024-01-31","cited_by_count":8,"area":null},{"id":"2409.17834","title":"PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification","created":"2024-09-26","cited_by_count":8,"area":null},{"id":"2404.03761","title":"Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks","created":"2024-04-04","cited_by_count":7,"area":null},{"id":"2105.13052","title":"A generalization of the randomized singular value decomposition","created":"2021-05-27","cited_by_count":6,"area":null},{"id":"2511.01924","title":"Neural Green's Functions","created":"2025-11-02","cited_by_count":6,"area":null},{"id":"2409.19221","title":"Cauchy activation function and XNet","created":"2024-09-28","cited_by_count":5,"area":null},{"id":"2210.10264","title":"SignReLU neural network and its approximation ability","created":"2022-10-19","cited_by_count":4,"area":null},{"id":"2302.10184","title":"AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios","created":"2023-02-05","cited_by_count":4,"area":null},{"id":"2403.12188","title":"PETScML: Second-order solvers for training regression problems in Scientific Machine Learning","created":"2024-03-18","cited_by_count":4,"area":null},{"id":"2407.03593","title":"Green Multigrid Network","created":"2024-07-04","cited_by_count":3,"area":null},{"id":"2412.16644","title":"An explainable operator approximation framework under the guideline of Green's function","created":"2024-12-21","cited_by_count":3,"area":null},{"id":"2501.02791","title":"Orthogonal greedy algorithm for linear operator learning with shallow neural network","created":"2025-01-06","cited_by_count":3,"area":null},{"id":"2504.13593","title":"KAN or MLP? Point Cloud Shows the Way Forward","created":"2025-04-18","cited_by_count":3,"area":null},{"id":"2509.11580","title":"Learning Singularity-Encoded Green's Functions with Application to Iterative Methods","created":"2025-09-15","cited_by_count":3,"area":null},{"id":"2606.09638","title":"Data-driven discovery of governing differential equations across physical systems","created":"2026-06-08","cited_by_count":3,"area":null},{"id":"2408.05540","title":"Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks","created":"2024-08-10","cited_by_count":2,"area":null},{"id":"2412.04064","title":"Graph Neural Networks Need Cluster-Normalize-Activate Modules","created":"2024-12-05","cited_by_count":2,"area":null},{"id":"2508.19672","title":"$\\mathcal{C}^1$-approximation with rational functions and rational neural networks","created":"2025-08-27","cited_by_count":2,"area":null},{"id":"2602.04006","title":"Rational ANOVA Networks","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2602.12390","title":"Rational Neural Networks have Expressivity Advantages","created":"2026-02-12","cited_by_count":2,"area":null},{"id":"2405.18203","title":"IAPT: Instruction-Aware Prompt Tuning for Large Language Models","created":"2024-05-28","cited_by_count":1,"area":null},{"id":"2501.18715","title":"chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data","created":"2025-01-30","cited_by_count":1,"area":null},{"id":"2601.18702","title":"From Fuzzy to Exact: The Halo Architecture for Infinite-Depth Reasoning via Rational Arithmetic","created":"2026-01-26","cited_by_count":1,"area":"lr"},{"id":"2602.18248","title":"Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2603.22951","title":"Weak-PDE-Net: Discovering Open-Form PDEs via Differentiable Symbolic Networks and Weak Formulation","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2605.03262","title":"A Universal Reproducing Kernel Hilbert Space from Polynomial Alignment and IMQ Distance","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2607.06781","title":"On Explicit Super-Expressive Approximation for Neural Networks","created":"2026-07-07","cited_by_count":1,"area":null},{"id":"2109.08813","title":"Data-driven rational function neural networks: a new method for generating analytical models of rock physics","created":"2021-09-18","cited_by_count":0,"area":null},{"id":"2301.11146","title":"Two-step interpretable modeling of Intensive Care Acquired Infections","created":"2023-01-26","cited_by_count":0,"area":null},{"id":"2301.13091","title":"Optimal Approximation Complexity of High-Dimensional Functions with Neural Networks","created":"2023-01-30","cited_by_count":0,"area":null},{"id":"2303.04436","title":"A comparison of rational and neural network based approximations","created":"2023-03-08","cited_by_count":0,"area":null},{"id":"2307.06287","title":"Rational Neural Network Controllers","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2310.13139","title":"The logic of rational graph neural networks","created":"2023-10-19","cited_by_count":0,"area":null},{"id":"2409.09217","title":"Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme","created":"2024-09-13","cited_by_count":0,"area":null},{"id":"2504.05366","title":"Handling Weather Uncertainty in Air Traffic Prediction through an Inverse Approach","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2506.19104","title":"On the algorithmic construction of deep ReLU networks","created":"2025-06-23","cited_by_count":0,"area":null},{"id":"2507.06765","title":"Robust Deep Network Learning of Nonlinear Regression Tasks by Parametric Leaky Exponential Linear Units (LELUs) and a Diffusion Metric","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2510.08456","title":"Integral Signatures of Activation Functions: A 9-Dimensional Taxonomy and Stability Theory for Deep Learning","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2601.06441","title":"FlexAct: Why Learn when you can Pick?","created":"2026-01-10","cited_by_count":0,"area":null},{"id":"2601.06777","title":"The Normalized Difference Layer: A Differentiable Spectral Index Formulation for Deep Learning","created":"2026-01-11","cited_by_count":0,"area":null},{"id":"2602.08419","title":"Radial M\\\"untz-Sz\\'asz Networks: Neural Architectures with Learnable Power Bases for Multidimensional Singularities","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2602.15603","title":"Symbolic recovery of PDEs from measurement data","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2603.20634","title":"CFNN: Continued Fraction Neural Network","created":"2026-03-21","cited_by_count":0,"area":null},{"id":"2606.00130","title":"Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Parameter Compression of Deep Neural Networks","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.14990","title":"Rational Sparse Autoencoder","created":"2026-06-12","cited_by_count":0,"area":"mechinterp"},{"id":"2608.25807","title":"Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.37015","title":"RBF-GNN: Rational Basis Functions for Pseudo-Coordinate based Graph Convolutions","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}