arXiv AI September 18, 2026 By Wladimir Silva

Green-ELM: Efficient Analytic Learning via High-Dimensional Random Projections

arXiv:2604. 15613v4 Announce Type: replace-cross Abstract: We present Green-ELM, a non-iterative neural architecture that replaces gradient-based optimization of the output layer with a closed-form analytic solution over a fixed, high-dimensional random feature representation.

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arXiv AI

Jul 29

CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning

arXiv:2509. 11285v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) in deep neural networks is conventionally framed as an iterative gradient-based optimization problem, incurring high computational cost, hyperparameter sensitivity, and risk of catastrophic forgetting.

By Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdi\~nas, Amparo Alonso-Betanzos

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arXiv Machine Learning

Jul 9

Efficient Long-Horizon Learning for Learned Optimization

arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.

By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky

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Efficient Long-Horizon Learning for Learned Optimization

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arXiv Machine Learning

Aug 31

Residual-Guided Randomized Neural Networks

arXiv:2608.28267v1 Announce Type: new Abstract: Randomized neural networks enable fast and analytically tractable training by fixing the input to hidden layer parameters at random and learning the ou...

By Mushir Akhtar, M. Tanveer, Mohd. Arshad

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arXiv Machine Learning

Aug 12

Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network

arXiv:2608. 10351v1 Announce Type: new Abstract: In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN).

By Karl Pierce, Yuehaw Khoo, Haizhao Yang

arXiv Machine Learning

Jul 24

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation

arXiv:2510. 24616v4 Announce Type: replace-cross Abstract: For four decades statistical physics has been providing a framework to analyse neural networks.

By Jean Barbier, Francesco Camilli, Minh-Toan Nguyen, Mauro Pastore, Rudy Skerk

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CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning

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