Morning Overview on MSN

Scientists build a ‘periodic table’ for AI models

Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a ...

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning BEIJING, Jan. 05, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the ...
Learn With Jay on MSNOpinion

Supervised learning made easy: Real-world example explained

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its ...
Scientific Research Publishing

Perin, E. L. S., De Souza, M. C., De Andrade Silva, J., & Matsubara, E. T. (2025). DynGraph-BERT: Combining BERT and GNN Using Dynamic Graphs for Inductive Semi-Supervised Text ...

ABSTRACT: Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional methods of contract analytics are time-consuming and often inexact, ...
IEEE

Self-Supervised Learning with Adaptive Graph Modeling for EEG-Based Epileptic Seizure Classification

Abstract: Objective: Epileptic seizure classification using EEG signals remains a significant challenge due to complex spatial-temporal dependencies, limited labeled data, and severe class imbalance.
IEEE

Self-Supervised Graph Masked Autoencoders for Hyperspectral Image Classification

Abstract: Traditional supervised deep learning (DL) methods for hyperspectral image (HSI) classification are severely limited by the quality and quantity of labels. Furthermore, existing feature ...
Frontiers

Enhanced visibility graph for EEG classification

Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions. In this paper, we propose an end-to-end framework ...
GitHub

Graph Positional Autoencoders as Self-supervised Learners (GraphPAE)

accelerate==0.33.0 numpy==1.24.4 ogb==1.3.6 pandas==2.2.3 PyYAML==6.0.2 rdkit_pypi==2022.9.5 scikit_learn==1.3.2 scipy==1.8.1 torch==2.1.2+cu121 torch_geometric==2.6.1 torch_scatter==2.1.2+pt21cu121 ...
Frontiers

Weakly supervised text classification on free-text comments in patient-reported outcome measures

Background: Free-text comments in patient-reported outcome measures (PROMs) data provide insights into health-related quality of life (HRQoL). However, these comments are typically analysed using ...