New model frames human reinforcement learning in the context of memory and habits

Humans and most other animals are known to be strongly driven by expected rewards or adverse consequences. The process of ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter ...
Tech Xplore

Overparameterized neural networks: Feature learning precedes overfitting, research finds

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...
Vietnam Investment Review on MSN

WiMi researches hybrid quantum classical learning for multi-class image classification

BEIJING, Dec. 5, 2025 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), is a leading global Hologram Augmented Reality ("AR") Technology provider. On the basis of ...
TMCnet

WiMi Studies Hybrid Quantum-Classical Learning Architecture for Multi-Class Image Classification

In this architecture, the training process adopts a joint optimization mechanism based on classical cross-entropy loss. WiMi treats the measurement probability distribution output by the quantum ...
EurekAlert!

How to accurately identify hard-to-detect foreign fibers in long-staple cotton?

Xinjiang long-staple cotton is widely used in the production of high-end textiles due to its excellent quality. However, ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Minkang Zhang Improves Medical Image Recognition Through RNN Optimization and Deep Learning Integration

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN ...
IEEE

Robust Adaptive Learning Rate Neural Network State Observer based Fixed-Time Sliding Mode Control of a Permanent Magnet Synchronous Motor

Abstract: This article proposes a robust controller based on fixed-time sliding mode control (FxTSMC) and a neural network to improve the speed tracking control performance of a permanent magnet ...
Frontiers

Research on short-term line loss rate prediction method of distribution network based on RF-CNN-LSTM

Under the background of the new distribution network, the power fluctuation on the line is increasing, which leads to more uncertainties in the predicted line loss rate, thus affecting the economic ...