Zobrazeno 1 - 10
of 355
pro vyhledávání: '"Sun Chenxi"'
Autor:
Li, Jun, Aguirre, Aaron, Moura, Junior, Liu, Che, Zhong, Lanhai, Sun, Chenxi, Clifford, Gari, Westover, Brandon, Hong, Shenda
Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model h
Externí odkaz:
http://arxiv.org/abs/2410.04133
Autor:
Sun, Chenxi, Zhang, Hongzhi, Lin, Zijia, Zhang, Jingyuan, Zhang, Fuzheng, Wang, Zhongyuan, Chen, Bin, Song, Chengru, Zhang, Di, Gai, Kun, Xiong, Deyi
Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequential nature of their decoding process, posing challenges for real-time
Externí odkaz:
http://arxiv.org/abs/2405.15208
Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models. A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence,
Externí odkaz:
http://arxiv.org/abs/2404.16886
We have developed an individual identification system based on magnetocardiography (MCG) signals captured using optically pumped magnetometers (OPMs). Our system utilizes pattern recognition to analyze the signals obtained at different positions on t
Externí odkaz:
http://arxiv.org/abs/2403.13820
Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption. However, it is difficult to demonstrate their superiority
Externí odkaz:
http://arxiv.org/abs/2401.10257
Time series widely exists in real-world applications and many deep learning models have performed well on it. Current research has shown the importance of learning strategy for models, suggesting that the benefit is the order and size of learning sam
Externí odkaz:
http://arxiv.org/abs/2312.15853
Autor:
Li, Tongxin, Sun, Chenxi
We tackle the challenge of learning to charge Electric Vehicles (EVs) with Out-of-Distribution (OOD) data. Traditional scheduling algorithms typically fail to balance near-optimal average performance with worst-case guarantees, particularly with OOD
Externí odkaz:
http://arxiv.org/abs/2311.05941
Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on developing FL algorithms, the effect of FL on the charging stations' op
Externí odkaz:
http://arxiv.org/abs/2310.08794
This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a fundamental large model, or fine-tunes a pre-trained LLM for TS data; TS-for-LLM (data-c
Externí odkaz:
http://arxiv.org/abs/2308.08241
While Current TTS systems perform well in synthesizing high-quality speech, producing highly expressive speech remains a challenge. Emphasis, as a critical factor in determining the expressiveness of speech, has attracted more attention nowadays. Pre
Externí odkaz:
http://arxiv.org/abs/2305.12107