{"total":63,"rows":[{"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":"2209.02128","title":"Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples","created":"2022-09-05","cited_by_count":69,"area":null},{"id":"2105.09513","title":"Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions","created":"2021-05-20","cited_by_count":49,"area":null},{"id":"2305.00241","title":"When Deep Learning Meets Polyhedral Theory: A Survey","created":"2023-04-29","cited_by_count":39,"area":null},{"id":"2209.02681","title":"How important are activation functions in regression and classification? A survey, performance comparison, and future directions","created":"2022-09-06","cited_by_count":29,"area":null},{"id":"2210.16222","title":"Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions","created":"2022-10-28","cited_by_count":24,"area":null},{"id":"2206.08615","title":"On the Number of Regions of Piecewise Linear Neural Networks","created":"2022-06-17","cited_by_count":21,"area":null},{"id":"2308.04073","title":"Learning Specialized Activation Functions for Physics-informed Neural Networks","created":"2023-08-08","cited_by_count":14,"area":null},{"id":"2103.15753","title":"Privacy and Trust Redefined in Federated Machine Learning","created":"2021-03-29","cited_by_count":13,"area":null},{"id":"2509.14026","title":"Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks","created":"2025-09-17","cited_by_count":10,"area":null},{"id":"2104.03693","title":"Learning specialized activation functions with the Piecewise Linear Unit","created":"2021-04-08","cited_by_count":7,"area":null},{"id":"2206.09149","title":"Piecewise Linear Neural Networks and Deep Learning","created":"2022-06-18","cited_by_count":7,"area":null},{"id":"2301.05785","title":"Efficient Activation Function Optimization through Surrogate Modeling","created":"2023-01-13","cited_by_count":5,"area":null},{"id":"2409.10463","title":"Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons","created":"2024-09-16","cited_by_count":5,"area":null},{"id":"2206.01332","title":"Optimal Activation Functions for the Random Features Regression Model","created":"2022-05-31","cited_by_count":4,"area":null},{"id":"2206.12166","title":"Neural Networks with A La Carte Selection of Activation Functions","created":"2022-06-24","cited_by_count":4,"area":null},{"id":"2301.00924","title":"Increasing biases can be more efficient than increasing weights","created":"2023-01-03","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":"2309.15244","title":"Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks","created":"2023-09-26","cited_by_count":4,"area":null},{"id":"2408.02743","title":"KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics example","created":"2024-08-05","cited_by_count":4,"area":null},{"id":"2509.13240","title":"Don't Forget the Nonlinearity: Unlocking Activation Functions in Efficient Fine-Tuning","created":"2025-09-16","cited_by_count":4,"area":null},{"id":"2211.04752","title":"Bayesian Neural Networks for Macroeconomic Analysis","created":"2022-11-09","cited_by_count":3,"area":null},{"id":"2309.16846","title":"Optimal Nonlinearities Improve Generalization Performance of Random Features","created":"2023-09-28","cited_by_count":3,"area":null},{"id":"2402.05401","title":"Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data","created":"2024-02-08","cited_by_count":3,"area":null},{"id":"2605.26647","title":"More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations","created":"2026-05-26","cited_by_count":3,"area":null},{"id":"2101.06848","title":"Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations","created":"2021-01-18","cited_by_count":2,"area":null},{"id":"2103.15898","title":"Comparison of different convolutional neural network activation functions and methods for building ensembles","created":"2021-03-29","cited_by_count":2,"area":null},{"id":"2211.14706","title":"Neural Network Verification as Piecewise Linear Optimization: Formulations for the Composition of Staircase Functions","created":"2022-11-27","cited_by_count":2,"area":null},{"id":"2301.05993","title":"Empirical study of the modulus as activation function in computer vision applications","created":"2023-01-15","cited_by_count":2,"area":null},{"id":"2412.17778","title":"From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology","created":"2024-12-23","cited_by_count":2,"area":null},{"id":"2104.00850","title":"Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation","created":"2021-04-02","cited_by_count":1,"area":null},{"id":"2108.00700","title":"Piecewise Linear Units Improve Deep Neural Networks","created":"2021-08-02","cited_by_count":1,"area":null},{"id":"2401.05439","title":"Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problems","created":"2024-01-08","cited_by_count":1,"area":null},{"id":"2403.15150","title":"An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning","created":"2024-03-22","cited_by_count":1,"area":null},{"id":"2404.10296","title":"Interpolating neural network: A novel unification of machine learning and interpolation theory","created":"2024-04-16","cited_by_count":1,"area":null},{"id":"2408.06820","title":"Efficient Search for Customized Activation Functions with Gradient Descent","created":"2024-08-13","cited_by_count":1,"area":null},{"id":"2408.13114","title":"Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures","created":"2024-08-23","cited_by_count":1,"area":null},{"id":"2106.13542","title":"Tensor-based framework for training flexible neural networks","created":"2021-06-25","cited_by_count":0,"area":null},{"id":"2108.09256","title":"PowerLinear Activation Functions with application to the first layer of CNNs","created":"2021-08-20","cited_by_count":0,"area":null},{"id":"2201.05089","title":"Using Computational Intelligence for solving the Ornstein-Zernike equation","created":"2021-11-17","cited_by_count":0,"area":null},{"id":"2204.04348","title":"Neuronal diversity can improve machine learning for physics and beyond","created":"2022-04-09","cited_by_count":0,"area":null},{"id":"2206.02819","title":"Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission","created":"2022-06-06","cited_by_count":0,"area":null},{"id":"2206.06322","title":"Tackling Multiple Tasks with One Single Learning Framework","created":"2022-05-29","cited_by_count":0,"area":null},{"id":"2304.03374","title":"Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization","created":"2023-04-06","cited_by_count":0,"area":null},{"id":"2306.04971","title":"A Melting Pot of Evolution and Learning","created":"2023-06-08","cited_by_count":0,"area":null},{"id":"2308.05724","title":"Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions","created":"2023-08-10","cited_by_count":0,"area":null},{"id":"2308.13670","title":"Linear Oscillation: A Novel Activation Function for Vision Transformer","created":"2023-08-25","cited_by_count":0,"area":null},{"id":"2401.01772","title":"A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure","created":"2024-01-03","cited_by_count":0,"area":null},{"id":"2402.09034","title":"Contrast-Enhanced Gating in GRUs for Robust Low-Data Sequence Learning","created":"2024-02-14","cited_by_count":0,"area":null},{"id":"2403.19896","title":"Nonlinearity Enhanced Adaptive Activation Functions","created":"2024-03-29","cited_by_count":0,"area":null},{"id":"2405.03712","title":"Your Network May Need to Be Rewritten: Network Adversarial Based on High-Dimensional Function Graph Decomposition","created":"2024-05-04","cited_by_count":0,"area":null},{"id":"2503.00340","title":"UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search","created":"2025-03-01","cited_by_count":0,"area":null},{"id":"2509.18161","title":"Developing Training Procedures for Piecewise-linear Spline Activation Functions in Neural Networks","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2601.10740","title":"Neuro-Symbolic Activation Discovery: Transferring Mathematical Structures from Physics to Ecology for Parameter-Efficient Neural Networks","created":"2026-01-10","cited_by_count":0,"area":null},{"id":"2601.18804","title":"Deep g-Pricing for CSI 300 Index Options with Volatility Trajectories and Market Sentiment","created":"2026-01-15","cited_by_count":0,"area":null},{"id":"2603.22154","title":"dynActivation: A Trainable Activation Family for Adaptive Nonlinearity","created":"2026-03-23","cited_by_count":0,"area":null},{"id":"2603.26096","title":"AcTTA: Rethinking Test-Time Adaptation via Dynamic Activation","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2603.28288","title":"FI-KAN: Fractal Interpolation Kolmogorov-Arnold Networks","created":"2026-03-30","cited_by_count":0,"area":null},{"id":"2606.13736","title":"Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2607.03148","title":"Rethinking Neural Nonlinearity as Gating","created":"2026-07-03","cited_by_count":0,"area":null},{"id":"2607.10735","title":"GNet: A scalable and flexible Gaussian process network with nonparametric neurons","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2608.11970","title":"TESLA: Taylor Expansion of Sinusoidal Learnable Activations","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2609.20465","title":"Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization","created":"2026-09-17","cited_by_count":0,"area":null}],"next_cursor":null}