output
20142026
most citedArtificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward

67 citations

Showing cs.LGShow all

10 papers · 1 filter

cs.LG2026

QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction

Emir Kaan Özdemir

Seismic prediction remains challenging due to the highly nonlinear and chaotic dynamics of earthquake signals. While classical deep learning models such as LSTMs and CNNs capture l…

cs.LG2026

Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding

Vasiliy S. Usatyuk, Denis A. Sapozhnikov, Sergey I. Egorov

We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without greedy search. NBSE constructs…

cs.LG2025★ 2 cited

Quantum-Enhanced Generative Models for Rare Event Prediction

M. Z. Haider, M. U. Ghouri, Tayyaba Noreen +1

Rare events such as financial crashes, climate extremes, and biological anomalies are notoriously difficult to model due to their scarcity and heavy-tailed distributions. Classical…

cs.LG2025

Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training

Ahmet Erdem Pamuk, Emir Kaan Özdemir, Şuayp Talha Kocabay

Large language models (LLMs) are increasingly trained with classical optimization techniques like AdamW to improve convergence and generalization. However, the mechanisms by which…

cs.LG2025★ 1 cited

Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networks

M. Z. Haider, Tayyaba Noreen, M. Salman

Blockchain Business applications and cryptocurrencies such as enable secure, decentralized value transfer, yet their pseudonymous nature creates opportunities for illicit activity,…

cs.LG2025

ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation

Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim +2

Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet, a novel end-to-end deep learning fram…