{"total":78,"rows":[{"id":"2102.01063","title":"Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition","created":"2021-02-01","cited_by_count":73,"area":null},{"id":"2105.14835","title":"Towards Lower Bounds on the Depth of ReLU Neural Networks","created":"2021-05-31","cited_by_count":56,"area":null},{"id":"2412.02975","title":"Theoretical limitations of multi-layer Transformer","created":"2024-12-04","cited_by_count":45,"area":null},{"id":"2110.03187","title":"On the Optimal Memorization Power of ReLU Neural Networks","created":"2021-10-07","cited_by_count":40,"area":null},{"id":"2302.12553","title":"Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes","created":"2023-02-24","cited_by_count":36,"area":null},{"id":"2208.05072","title":"Interpretable Polynomial Neural Ordinary Differential Equations","created":"2022-08-09","cited_by_count":24,"area":null},{"id":"2205.13863","title":"Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power","created":"2022-05-27","cited_by_count":23,"area":null},{"id":"2102.01621","title":"Depth separation beyond radial functions","created":"2021-02-02","cited_by_count":18,"area":null},{"id":"2112.02393","title":"Optimization-Based Separations for Neural Networks","created":"2021-12-04","cited_by_count":17,"area":null},{"id":"2109.13359","title":"Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximation","created":"2021-09-27","cited_by_count":16,"area":null},{"id":"2204.01368","title":"Training Fully Connected Neural Networks is $\\exists\\mathbb{R}$-Complete","created":"2022-04-04","cited_by_count":15,"area":null},{"id":"2106.09309","title":"Layer Folding: Neural Network Depth Reduction using Activation Linearization","created":"2021-06-17","cited_by_count":14,"area":null},{"id":"2110.07641","title":"Non-deep Networks","created":"2021-10-14","cited_by_count":14,"area":null},{"id":"2506.03951","title":"Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective","created":"2025-06-04","cited_by_count":14,"area":null},{"id":"2507.12549","title":"The Serial Scaling Hypothesis","created":"2025-07-16","cited_by_count":13,"area":null},{"id":"2102.06635","title":"ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation","created":"2021-02-12","cited_by_count":12,"area":null},{"id":"2112.09747","title":"A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation","created":"2021-12-17","cited_by_count":12,"area":null},{"id":"2307.09212","title":"How Many Neurons Does it Take to Approximate the Maximum?","created":"2023-07-18","cited_by_count":12,"area":null},{"id":"2402.15315","title":"On Minimal Depth in Neural Networks","created":"2024-02-23","cited_by_count":11,"area":null},{"id":"2201.11137","title":"Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization","created":"2022-01-26","cited_by_count":10,"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":"2212.05427","title":"Statistical guarantees for sparse deep learning","created":"2022-12-11","cited_by_count":9,"area":null},{"id":"2304.01063","title":"Depth Separation with Multilayer Mean-Field Networks","created":"2023-04-03","cited_by_count":9,"area":null},{"id":"2306.11918","title":"Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback","created":"2023-06-20","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":"2205.07394","title":"Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning","created":"2022-05-15","cited_by_count":8,"area":null},{"id":"2102.00314","title":"Size and Depth Separation in Approximating Benign Functions with Neural Networks","created":"2021-01-30","cited_by_count":7,"area":null},{"id":"2103.00542","title":"Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on H\\\"older Class","created":"2021-02-28","cited_by_count":7,"area":null},{"id":"2106.14749","title":"A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning","created":"2021-06-28","cited_by_count":7,"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":"2208.12190","title":"CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning","created":"2022-08-25","cited_by_count":6,"area":null},{"id":"2303.15544","title":"Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach","created":"2023-03-27","cited_by_count":6,"area":null},{"id":"2307.04001","title":"Polynomial Width is Sufficient for Set Representation with High-dimensional Features","created":"2023-07-08","cited_by_count":6,"area":null},{"id":"2405.02561","title":"Understanding the Difficulty of Solving Cauchy Problems with PINNs","created":"2024-05-04","cited_by_count":6,"area":null},{"id":"2204.03931","title":"HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems","created":"2022-04-08","cited_by_count":5,"area":null},{"id":"2302.00045","title":"Neural Control of Parametric Solutions for High-dimensional Evolution PDEs","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2101.04243","title":"A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks","created":"2021-01-12","cited_by_count":4,"area":null},{"id":"2104.05021","title":"CovNet: Covariance Networks for Functional Data on Multidimensional Domains","created":"2021-04-11","cited_by_count":4,"area":null},{"id":"2201.05395","title":"De Rham compatible Deep Neural Network FEM","created":"2022-01-14","cited_by_count":4,"area":null},{"id":"2202.01919","title":"Theoretical Exploration of Solutions of Feedforward ReLU Networks","created":"2022-01-24","cited_by_count":4,"area":null},{"id":"2210.10264","title":"SignReLU neural network and its approximation ability","created":"2022-10-19","cited_by_count":4,"area":null},{"id":"2301.04605","title":"Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions","created":"2023-01-11","cited_by_count":4,"area":null},{"id":"2307.00673","title":"ENN: A Neural Network with DCT Adaptive Activation Functions","created":"2023-07-02","cited_by_count":4,"area":null},{"id":"2402.07248","title":"Depth Separations in Neural Networks: Separating the Dimension from the Accuracy","created":"2024-02-11","cited_by_count":4,"area":null},{"id":"2208.04846","title":"Mining Reaction and Diffusion Dynamics in Social Activities","created":"2022-08-07","cited_by_count":3,"area":null},{"id":"2209.03450","title":"Seeking Interpretability and Explainability in Binary Activated Neural Networks","created":"2022-09-07","cited_by_count":3,"area":null},{"id":"2402.08808","title":"Depth Separation in Norm-Bounded Infinite-Width Neural Networks","created":"2024-02-13","cited_by_count":3,"area":null},{"id":"2601.01417","title":"A Depth Hierarchy for Computing the Maximum in ReLU Networks via Extremal Graph Theory","created":"2026-01-04","cited_by_count":3,"area":null},{"id":"2108.10825","title":"Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data","created":"2021-08-24","cited_by_count":2,"area":null},{"id":"2405.15150","title":"Enhancing Learning with Label Differential Privacy by Vector Approximation","created":"2024-05-24","cited_by_count":2,"area":null},{"id":"2112.08070","title":"Depth Refinement for Improved Stereo Reconstruction","created":"2021-12-15","cited_by_count":1,"area":null},{"id":"2201.06658","title":"Learning Neural Ranking Models Online from Implicit User Feedback","created":"2022-01-17","cited_by_count":1,"area":null},{"id":"2208.07498","title":"Universal Solutions of Feedforward ReLU Networks for Interpolations","created":"2022-08-16","cited_by_count":1,"area":null},{"id":"2211.12670","title":"Expressibility-Enhancing Strategies for Quantum Neural Networks","created":"2022-11-23","cited_by_count":1,"area":null},{"id":"2303.03544","title":"Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation","created":"2023-03-06","cited_by_count":1,"area":null},{"id":"2406.08933","title":"LaCoOT: Layer Collapse through Optimal Transport","created":"2024-06-13","cited_by_count":1,"area":null},{"id":"2103.12686","title":"PAC-learning gains of Turing machines over circuits and neural networks","created":"2021-03-23","cited_by_count":0,"area":null},{"id":"2106.12532","title":"Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise","created":"2021-06-23","cited_by_count":0,"area":null},{"id":"2108.03120","title":"Stochastic Deep Model Reference Adaptive Control","created":"2021-08-04","cited_by_count":0,"area":null},{"id":"2111.08438","title":"Fourier Neural Networks for Function Approximation","created":"2021-10-21","cited_by_count":0,"area":null},{"id":"2112.04314","title":"A systematic approach to random data augmentation on graph neural networks","created":"2021-12-08","cited_by_count":0,"area":null},{"id":"2203.08174","title":"Towards understanding deep learning with the natural clustering prior","created":"2022-03-15","cited_by_count":0,"area":null},{"id":"2301.08360","title":"Domain-adapted Learning and Imitation: DRL for Power Arbitrage","created":"2023-01-19","cited_by_count":0,"area":null},{"id":"2302.07405","title":"Unsupervised physics-informed neural network in reaction-diffusion biology models (Ulcerative colitis and Crohn's disease cases) A preliminary study","created":"2023-02-15","cited_by_count":0,"area":null},{"id":"2310.19938","title":"Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller","created":"2023-10-30","cited_by_count":0,"area":null},{"id":"2311.14731","title":"Deep State-Space Model for Predicting Cryptocurrency Price","created":"2023-11-21","cited_by_count":0,"area":null},{"id":"2401.10385","title":"Approximation of Solution Operators for High-dimensional PDEs","created":"2024-01-18","cited_by_count":0,"area":null},{"id":"2410.21111","title":"LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2502.15306","title":"A Data-Driven Real-Time Optimal Power Flow Algorithm Using Local Feedback","created":"2025-02-21","cited_by_count":0,"area":null},{"id":"2503.23814","title":"An extension of linear self-attention for in-context learning","created":"2025-03-31","cited_by_count":0,"area":null},{"id":"2507.22316","title":"LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstruction","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2509.04477","title":"Universal Representation of Generalized Convex Functions and their Gradients","created":"2025-08-30","cited_by_count":0,"area":null},{"id":"2509.06694","title":"Barycentric Neural Networks and Length-Weighted Persistent Entropy Loss: A Green Geometric and Topological Framework for Function Approximation","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2512.13751","title":"MIDUS: Memory-Infused Depth Up-Scaling","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2601.18264","title":"Neural Network Approximation: A View from Polytope Decomposition","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2607.10589","title":"Approximation of Analytic Functions by ReLU Neural Networks with Adjustable Depth and Width","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2608.23877","title":"Every Layer Counts: An Exponential $L_2$ Depth Hierarchy for ReLU Networks","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.25874","title":"Neural Approximation by Function Composition: Rigidity and Doubly Exponential Convergence","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}