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Securities regulator urges market players to develop new strategies and nail cyber-basics before AI models fuel mass attacks ...
Frontiers

Biologically plausible learning in spiking neural networks

Spiking Neural Networks (SNNs) represent the "third generation" of neural models, capturing the discrete, asynchronous, and energy-efficient nature of ...
Tech Xplore

Novel approach to training AI saves energy, improves speed, and minimizes data sent across networks

In a novel attempt to improve how large language models learn and make them more capable and energy-efficient, Stevens ...
TMCnet

MicroAlgo Inc. Develops Quantum Algorithm Technology for Feedforward Neural Networks to Drive Neural Network Revolution

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that they have developed a set of quantum algorithms for feedforward neural networks, breaking through the performance ...
eLife

Adjoint propagation of error signal through modular recurrent neural networks for biologically plausible learning

Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...
IEEE

Multilabel Emotion Recognition in Urdu Using Attention-Based Hybrid Model and Fractional Order Backpropagation

Abstract: Emotion detection in text has been widely researched for high-resource languages, yet low-resource languages such as Urdu remain underexplored. The present work addresses this gap by ...
Psychology Today

Why the New Artificial Intelligence Is So Powerful

More than a billion people are now using artificial intelligence (AI) models regularly, for purposes ranging from work to advice about personal relationships. This trend began with the introduction of ...
Scientific Research Publishing

Magoulas, G.D., Vrahatis, M.N. and Androulakis, G.S. (1999) Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods. Neural ...

ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...
IEEE

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines

Abstract: Deep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets ...