Frontiers

SeismoQuakeGNN: a hybrid framework for spatio-temporal earthquake prediction with transformer-enhanced models

Accurate predictions of earthquakes are crucial for disaster preparedness and risk mitigation. Conventional machine learning models like Random Forest, SVR, and XGBoost are frequently used for seismic ...
Frontiers

Determining the meter of classical Arabic poetry using deep learning: a performance analysis

1 Department of Computer Engineering, College of Engineering and Petroleum, Kuwait University, Safat, Kuwait 2 Department of Computer Sciences, University of Hamburg, Hamburg, Germany The metrical ...
Microsoft

Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification

Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoint. In this paper, ...
marktechpost

Apple Researchers Propose a Novel AI Algorithm to Optimize a Byte-Level Representation for Automatic Speech Recognition ASR and Compare it with UTF-8 Representation

End-to-end (E2E) neural networks have emerged as flexible and accurate models for multilingual automatic speech recognition (ASR). However, as the number of supported languages increases, particularly ...
IEEE

Graph Convolutional Multi-Label Hashing for Cross-Modal Retrieval

Abstract: Cross-modal hashing encodes different modalities of multimodal data into low-dimensional Hamming space for fast cross-modal retrieval. In multi-label cross-modal retrieval, multimodal data ...