Transformer-based models have become state-of-the-art tools in various machine learning tasks, including time series classification, yet their complexity makes ...
Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary matrix multip...
This study proposes Tool-RoCo, a novel benchmark for evaluating large language models (LLMs) in long-term multi-agent cooperation based on RoCo, a multi-robot c...
Translating non-invasive signals such as photoplethysmography (PPG) and ballistocardiography (BCG) into clinically meaningful signals like arterial blood pressu...
We present a novel training approach, named Merge-and-Bound (M&B) for Class Incremental Learning (CIL), which directly manipulates model weights in the para...
Readability assessment aims to evaluate the reading difficulty of a text. In recent years, while deep learning technology has been gradually applied to readabil...
Spatial cognition is fundamental to real-world multimodal intelligence, allowing models to effectively interact with the physical environment. While multimodal ...
We study two-layer neural networks and train these with a particle-based method called consensus-based optimization (CBO). We compare the performance of CBO aga...
Ensuring the safety of embodied AI agents during task planning is critical for real-world deployment, especially in household environments where dangerous instr...
Remote sensing change captioning is an emerging and popular research task that aims to describe, in natural language, the content of interest that has changed b...
Text-attributed graphs require models to effectively combine strong textual understanding with structurally informed reasoning. Existing approaches either rely ...
Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are popular methods for function approximation due to their flexibility and expressivity. Howe...
