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 ...
eLife

Deep learning reveals endogenous sterols as allosteric modulators of the GPCR–Gα interface

An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...
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

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), is a leading global Hologram Augmented Reality ("AR") Technology provider. On the basis of in-depth research on quantum convolutional ...
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, ...

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

Layer-Wise Training of Graph Neural Networks With Self-Supervised Learning

Abstract: Training graph neural networks (GNNs) on large graphs is challenging due to both the high memory and computational costs of end-to-end training and the scarcity of detailed node-level ...
IEEE

Fault-Resilient RIS Systems: Toward SINR-and Sum-Rate Maximization of IoT Networks Using ML Frameworks

Abstract: Pixel failures on Reconfigurable Intelligent Surfaces (RISs) cause major challenges in wireless communication systems. These failures are especially problematic for Internet of Things (IoT) ...