Zobrazeno 1 - 10
of 1 172 288
pro vyhledávání: '"Tan As"'
Autor:
Feng, Peijie, Tan, Yong, Chong, Mingzhe, Li, Lintao, Zhang, Zongkun, Liu, Fubei, Tan, Yunhua, Wen, Yongzheng
Diffractive deep neural network (D2NN), known for its high speed, low power consumption, and strong parallelism, has been widely applied across various fields, including pattern recognition, image processing, and image transmission. However, existing
Externí odkaz:
http://arxiv.org/abs/2409.20346
Autor:
Yan, Jipeng, Kawara, Shusei, Tan, Qingyuan, Zhu, Jingwen, Wang, Bingxue, Toulemonde, Matthieu, Liu, Honghai, Tan, Ying, Tang, Meng-Xing
Super-Resolution Ultrasound (SRUS) imaging through localising and tracking microbubbles, also known as Ultrasound Localisation Microscopy (ULM), has demonstrated significant potential for reconstructing microvasculature and flows with sub-diffraction
Externí odkaz:
http://arxiv.org/abs/2409.11391
Autor:
Wang, Xin, Tan, Tao, Gao, Yuan, Marcus, Eric, Han, Luyi, Portaluri, Antonio, Zhang, Tianyu, Lu, Chunyao, Liang, Xinglong, Beets-Tan, Regina, Teuwen, Jonas, Mann, Ritse
Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention. Despite the promising potential of recent mammogram (MG) based deep learning models in predicting BC risk, they mostly overlook the 'time-
Externí odkaz:
http://arxiv.org/abs/2409.06887
With the increasing integration of inverter-based resources into the power grid, there has been a notable reduction in system inertia, potentially compromising frequency stability. To assess the suitability of existing area inertia estimation techniq
Externí odkaz:
http://arxiv.org/abs/2408.00511
Autor:
Teo, Rachel S. Y., Nguyen, Tan M.
Sparse Mixture of Experts (SMoE) has become the key to unlocking unparalleled scalability in deep learning. SMoE has the potential to exponentially increase parameter count while maintaining the efficiency of the model by only activating a small subs
Externí odkaz:
http://arxiv.org/abs/2410.14574
Large Language Models (LLMs) have achieved impressive results in various tasks but struggle with hallucination problems and lack of relevant knowledge, especially in deep complex reasoning and knowledge-intensive tasks. Knowledge Graphs (KGs), which
Externí odkaz:
http://arxiv.org/abs/2410.14211
Time series forecasting aids decision-making, especially for stakeholders who rely on accurate predictions, making it very important to understand and explain these models to ensure informed decisions. Traditional explainable AI (XAI) methods, which
Externí odkaz:
http://arxiv.org/abs/2410.14180
Autor:
Zhang, Mozhi, Wang, Pengyu, Tan, Chenkun, Huang, Mianqiu, Zhang, Dong, Zhou, Yaqian, Qiu, Xipeng
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications. To make LLMs more usable, aligning them with human preferences is essential. Existing
Externí odkaz:
http://arxiv.org/abs/2410.14184
When assisting people in daily tasks, robots need to accurately interpret visual cues and respond effectively in diverse safety-critical situations, such as sharp objects on the floor. In this context, we present M-CoDAL, a multimodal-dialogue system
Externí odkaz:
http://arxiv.org/abs/2410.14141
Autor:
Zhang, Boyuan, Fang, Bo, Ye, Fanjiang, Gu, Yida, Tallent, Nathan, Tan, Guangming, Tao, Dingwen
Full-state quantum circuit simulation requires exponentially increased memory size to store the state vector as the number of qubits scales, presenting significant limitations in classical computing systems. Our paper introduces BMQSim, a novel state
Externí odkaz:
http://arxiv.org/abs/2410.14088