Function Approximation - Edgentiq

KANalogue: A Fully Analog Approach to AI Computation

Kolmogorov-Arnold Networks: A Deep Dive into a Flexible AI Architecture

New Research Reveals Computational Limits of Flexible Reinforcement Learning Models

CauchyNet: A New Neural Network for Efficient Learning with Complex Data

Unlocking Real-World Impact: The Theory of Offline Reinforcement Learning

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Advancing Constrained Reinforcement Learning with a Novel Natural Critic-Actor Algorithm

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Spline-Based KANs Achieve Optimal Learning Rates

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Barycentric Neural Networks and Length-Weighted Persistent Entropy: A New Framework for Efficient Function Approximation

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Chebyshev Polynomials Enhance Deep Q-Networks for More Efficient AI Learning

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MJKAN: A Hybrid Neural Network Bridging KAN and MLP for Enhanced Efficiency and Expressiveness

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KANalogue: A Fully Analog Approach to AI Computation

October 31, 2025

Kolmogorov-Arnold Networks: A Deep Dive into a Flexible AI Architecture

October 31, 2025

New Research Reveals Computational Limits of Flexible Reinforcement Learning Models

October 28, 2025