{"total":89,"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":"2406.07456","title":"fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions","created":"2024-06-11","cited_by_count":64,"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":"2309.11932","title":"A Machine Learning-oriented Survey on Tiny Machine Learning","created":"2023-09-21","cited_by_count":27,"area":null},{"id":"2205.09459","title":"Neural Network Architecture Beyond Width and Depth","created":"2022-05-19","cited_by_count":17,"area":null},{"id":"2305.12073","title":"GELU Activation Function in Deep Learning: A Comprehensive Mathematical Analysis and Performance","created":"2023-05-20","cited_by_count":16,"area":null},{"id":"2407.12895","title":"A Survey on Universal Approximation Theorems","created":"2024-07-17","cited_by_count":15,"area":null},{"id":"2401.10967","title":"HOSC: A Periodic Activation Function for Preserving Sharp Features in Implicit Neural Representations","created":"2024-01-20","cited_by_count":14,"area":null},{"id":"2503.10065","title":"Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild","created":"2025-03-13","cited_by_count":13,"area":null},{"id":"2112.13544","title":"FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions","created":"2021-12-27","cited_by_count":12,"area":null},{"id":"2405.01725","title":"Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey","created":"2024-05-02","cited_by_count":10,"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":"2311.03733","title":"Improved weight initialization for deep and narrow feedforward neural network","created":"2023-11-07","cited_by_count":7,"area":null},{"id":"2407.09580","title":"Don't Fear Peculiar Activation Functions: EUAF and Beyond","created":"2024-07-12","cited_by_count":7,"area":null},{"id":"2411.15111","title":"Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations","created":"2024-11-22","cited_by_count":7,"area":null},{"id":"2204.02921","title":"A survey on recently proposed activation functions for Deep Learning","created":"2022-04-06","cited_by_count":6,"area":null},{"id":"2407.07564","title":"Trainable Highly-expressive Activation Functions","created":"2024-07-10","cited_by_count":6,"area":null},{"id":"2103.10492","title":"Recent Advances in Deep Learning Techniques for Face Recognition","created":"2021-03-18","cited_by_count":5,"area":null},{"id":"2109.08958","title":"AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks","created":"2021-09-18","cited_by_count":5,"area":null},{"id":"2207.10817","title":"End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge","created":"2022-07-20","cited_by_count":5,"area":null},{"id":"2301.05785","title":"Efficient Activation Function Optimization through Surrogate Modeling","created":"2023-01-13","cited_by_count":5,"area":null},{"id":"2302.00722","title":"A Survey of Deep Learning: From Activations to Transformers","created":"2023-02-01","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":"2111.13858","title":"Why KDAC? A general activation function for knowledge discovery","created":"2021-11-27","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":"2306.00689","title":"Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2405.20768","title":"Expanded Gating Ranges Improve Activation Functions","created":"2024-05-25","cited_by_count":4,"area":null},{"id":"2306.01822","title":"ErfReLU: Adaptive Activation Function for Deep Neural Network","created":"2023-06-02","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":"2408.00386","title":"What comes after transformers? -- A selective survey connecting ideas in deep learning","created":"2024-08-01","cited_by_count":3,"area":null},{"id":"2412.03682","title":"Designing DNNs for a trade-off between robustness and processing performance in embedded devices","created":"2024-12-04","cited_by_count":3,"area":null},{"id":"2504.13786","title":"On the Relationship Between Robustness and Expressivity of Graph Neural Networks","created":"2025-04-18","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":"2206.09040","title":"Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach","created":"2022-06-17","cited_by_count":2,"area":null},{"id":"2209.12737","title":"A connection between probability, physics and neural networks","created":"2022-09-26","cited_by_count":2,"area":null},{"id":"2401.13652","title":"Graph-Instructed Neural Networks for Sparse Grid-Based Discontinuity Detectors","created":"2024-01-24","cited_by_count":2,"area":null},{"id":"2407.02013","title":"DiGRAF: Diffeomorphic Graph-Adaptive Activation Function","created":"2024-07-02","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":"2505.09486","title":"Preserving Plasticity in Continual Learning with Adaptive Linearity Injection","created":"2025-05-14","cited_by_count":2,"area":null},{"id":"2607.17624","title":"Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks","created":"2026-07-20","cited_by_count":2,"area":null},{"id":"2109.00903","title":"Effect of the output activation function on the probabilities and errors in medical image segmentation","created":"2021-09-02","cited_by_count":1,"area":null},{"id":"2110.02775","title":"NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks","created":"2021-10-05","cited_by_count":1,"area":null},{"id":"2112.02997","title":"Language Semantics Interpretation with an Interaction-based Recurrent Neural Networks","created":"2021-11-02","cited_by_count":1,"area":null},{"id":"2208.03616","title":"Transmission Neural Networks: From Virus Spread Models to Neural Networks","created":"2022-08-07","cited_by_count":1,"area":null},{"id":"2305.10964","title":"Learning Activation Functions for Sparse Neural Networks","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2306.01991","title":"A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science","created":"2023-06-03","cited_by_count":1,"area":null},{"id":"2306.06146","title":"Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers","created":"2023-06-09","cited_by_count":1,"area":null},{"id":"2310.14354","title":"Toward Flare-Free Images: A Survey","created":"2023-10-22","cited_by_count":1,"area":null},{"id":"2401.12002","title":"HgbNet: predicting hemoglobin level/anemia degree from EHR data","created":"2024-01-22","cited_by_count":1,"area":null},{"id":"2402.09249","title":"Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions","created":"2024-02-14","cited_by_count":1,"area":null},{"id":"2405.10563","title":"Function Extrapolation with Neural Networks and Its Application for Manifolds","created":"2024-05-17","cited_by_count":1,"area":null},{"id":"2406.16481","title":"Improving Quaternion Neural Networks with Quaternionic Activation Functions","created":"2024-06-24","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":"2409.04915","title":"Activation Function Optimization Scheme for Image Classification","created":"2024-09-07","cited_by_count":1,"area":null},{"id":"2412.08286","title":"Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction","created":"2024-12-11","cited_by_count":1,"area":null},{"id":"2503.15209","title":"Kolmogorov-Arnold Network for Transistor Compact Modeling","created":"2025-03-19","cited_by_count":1,"area":null},{"id":"2508.17069","title":"Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration","created":"2025-08-23","cited_by_count":1,"area":null},{"id":"2508.18912","title":"HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2510.03576","title":"BEKAN: Boundary condition-guaranteed evolutionary Kolmogorov-Arnold networks with radial basis functions for solving PDE problems","created":"2025-10-03","cited_by_count":1,"area":null},{"id":"2510.08795","title":"PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2510.15778","title":"Controlling the image generation process with parametric activation functions","created":"2025-10-17","cited_by_count":1,"area":null},{"id":"2602.18515","title":"Weak-Form Evolutionary Kolmogorov-Arnold Networks for Solving Partial Differential Equations","created":"2026-02-19","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":"2204.04348","title":"Neuronal diversity can improve machine learning for physics and beyond","created":"2022-04-09","cited_by_count":0,"area":null},{"id":"2210.17314","title":"A deep scalable neural architecture for soil properties estimation from spectral information","created":"2022-10-26","cited_by_count":0,"area":null},{"id":"2211.06878","title":"Evaluating CNN with Oscillatory Activation Function","created":"2022-11-13","cited_by_count":0,"area":null},{"id":"2304.04455","title":"Bayesian optimization for sparse neural networks with trainable activation functions","created":"2023-04-10","cited_by_count":0,"area":null},{"id":"2304.11758","title":"Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)","created":"2023-04-23","cited_by_count":0,"area":null},{"id":"2306.03531","title":"A Unified Concept-Based System for Local, Global, and Misclassification Explanations","created":"2023-06-06","cited_by_count":0,"area":null},{"id":"2310.09101","title":"Privacy-Preserving Encrypted Low-Dose CT Denoising","created":"2023-10-13","cited_by_count":0,"area":null},{"id":"2402.07735","title":"Graph Structure Inference with BAM: Introducing the Bilinear Attention Mechanism","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2403.08589","title":"Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?","created":"2024-03-13","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":"2405.18805","title":"Semiring Activation in Neural Networks","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2501.05494","title":"Soft Computing Approaches for Predicting Shade-Seeking Behaviour in Dairy Cattle under Heat Stress: A Comparative Study of Random Forests and Neural Networks","created":"2025-01-09","cited_by_count":0,"area":null},{"id":"2507.06417","title":"Capsule-ConvKAN: A Hybrid Neural Approach to Medical Image Classification","created":"2025-07-08","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":"2507.11493","title":"A parametric activation function based on Wendland RBF","created":"2025-06-28","cited_by_count":0,"area":null},{"id":"2507.14270","title":"APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation","created":"2025-07-18","cited_by_count":0,"area":null},{"id":"2510.03266","title":"Variational Autoencoders-based Detection of Extremes in Plant Productivity in an Earth System Model","created":"2025-09-26","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":"2510.22450","title":"SmartMixed: A Two-Phase Training Strategy for Adaptive Activation Function Learning in Neural Networks","created":"2025-10-25","cited_by_count":0,"area":null},{"id":"2512.20006","title":"Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance","created":"2025-12-23","cited_by_count":0,"area":null},{"id":"2602.13864","title":"Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation","created":"2026-02-14","cited_by_count":0,"area":null},{"id":"2602.22003","title":"Neural solver for Wasserstein Geodesics and optimal transport dynamics","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2603.18328","title":"A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networks","created":"2026-03-18","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":"2604.00168","title":"Neural-Assisted in-Motion Self-Heading Alignment","created":"2026-03-31","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}],"next_cursor":null}