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...
Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning toke...
Recently, video generation has witnessed rapid advancements, drawing increasing attention to image-to-video (I2V) synthesis on mobile devices. However, the subs...
The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body ...
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...
Ensemble learning improves classification performance by combining multiple base classifiers. While increasing the number of classifiers generally enhances accu...
Ensuring the safety of embodied AI agents during task planning is critical for real-world deployment, especially in household environments where dangerous instr...
Event cameras produce asynchronous event streams that are spatially sparse yet temporally dense. Mainstream event representation learning algorithms typically u...
Model merging combines multiple fine-tuned checkpoints into a single model without additional training, offering an attractive approach to reusing models and ef...
