arXiv:2509.10530v1 Announce Type: new Abstract: Transformer models based on the Mixture of Experts (MoE) architecture have made significant progress in long-sequence modeling, but existing models still have shortcomings in computational efficiency and the ability to capture long-range dependencies, especially in…
arXiv:2504.20022v2 Announce Type: replace-cross Abstract: Multilingual Large Language Models (LLMs) have demonstrated significant effectiveness across various languages, particularly in high-resource languages such as English. However, their performance in terms of factual accuracy across other low-resource languages, especially Indic languages, remains…
arXiv:2509.10529v1 Announce Type: new Abstract: Continual learning — the ability to acquire knowledge incrementally without forgetting previous skills — is fundamental to natural intelligence. While the human brain excels at this, artificial neural networks struggle with “catastrophic forgetting,” where learning…
arXiv:2509.10528v1 Announce Type: new Abstract: Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for…
arXiv:2408.14435v2 Announce Type: replace-cross Abstract: We explore social perception of human faces in CLIP, a widely used open-source vision-language model. To this end, we compare the similarity in CLIP embeddings between different textual prompts and a set of face images.…
arXiv:2509.10526v1 Announce Type: new Abstract: This paper presents a novel approach to neural network pruning by integrating a graph-based observation space into an AutoML framework to address the limitations of existing methods. Traditional pruning approaches often depend on hand-crafted heuristics…
arXiv:2203.03221v3 Announce Type: replace-cross Abstract: Clustering in directed graphs remains a fundamental challenge due to the asymmetry in edge connectivity, which limits the applicability of classical spectral methods originally designed for undirected graphs. A common workaround is to symmetrize the…
arXiv:2509.10523v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical brain maturation. However, the adaptation of transfer learning paradigms in machine learning for ASD research remains notably limited. In this study, we propose a…
arXiv:2509.00174v2 Announce Type: replace Abstract: Recent progress in deep learning has been driven by increasingly larger models. However, their computational and energy demands have grown proportionally, creating significant barriers to their deployment and to a wider adoption of deep learning…
arXiv:2509.10522v1 Announce Type: new Abstract: Air traffic controllers (ATCOs) issue high-intensity voice commands in dense airspace, where accurate workload modeling is critical for safety and efficiency. This paper proposes a multimodal deep learning framework that integrates structured data, trajectory sequences,…
