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pro vyhledávání: '"ZHANG, Dong"'
In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items.
Externí odkaz:
http://arxiv.org/abs/2411.00341
Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. While scaling laws have enhanced LLM capabilities, the primary bottleneck has shifted from \textit
Externí odkaz:
http://arxiv.org/abs/2410.23918
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
Autor:
Zhang, Xin, Lyu, Xiang, Du, Zhihao, Chen, Qian, Zhang, Dong, Hu, Hangrui, Tan, Chaohong, Zhao, Tianyu, Wang, Yuxuan, Zhang, Bin, Lu, Heng, Zhou, Yaqian, Qiu, Xipeng
Current methods of building LLMs with voice interaction capabilities rely heavily on explicit text autoregressive generation before or during speech response generation to maintain content quality, which unfortunately brings computational overhead an
Externí odkaz:
http://arxiv.org/abs/2410.08035
Living fish may suddenly encounter upstream obstacles, join the queue of the fish schooling, or detect upstream flow in advance, resulting in interactions with environmental vortices that can be abrupt or develop gradually from an initial state. The
Externí odkaz:
http://arxiv.org/abs/2409.17957
Autor:
Shi, Jiatong, Tian, Jinchuan, Wu, Yihan, Jung, Jee-weon, Yip, Jia Qi, Masuyama, Yoshiki, Chen, William, Wu, Yuning, Tang, Yuxun, Baali, Massa, Alharhi, Dareen, Zhang, Dong, Deng, Ruifan, Srivastava, Tejes, Wu, Haibin, Liu, Alexander H., Raj, Bhiksha, Jin, Qin, Song, Ruihua, Watanabe, Shinji
Neural codecs have become crucial to recent speech and audio generation research. In addition to signal compression capabilities, discrete codecs have also been found to enhance downstream training efficiency and compatibility with autoregressive lan
Externí odkaz:
http://arxiv.org/abs/2409.15897
Partially-supervised multi-organ medical image segmentation aims to develop a unified semantic segmentation model by utilizing multiple partially-labeled datasets, with each dataset providing labels for a single class of organs. However, the limited
Externí odkaz:
http://arxiv.org/abs/2409.03228
Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising generalization ability. In this work, we propose ViP, a novel visual sympt
Externí odkaz:
http://arxiv.org/abs/2409.00341
The increasing demand for medical imaging has surpassed the capacity of available radiologists, leading to diagnostic delays and potential misdiagnoses. Artificial intelligence (AI) techniques, particularly in automatic medical report generation (AMR
Externí odkaz:
http://arxiv.org/abs/2408.13988
Autor:
Li, Zhaowei, Wang, Wei, Cai, YiQing, Qi, Xu, Wang, Pengyu, Zhang, Dong, Song, Hang, Jiang, Botian, Huang, Zhida, Wang, Tao
Significant advancements has recently been achieved in the field of multi-modal large language models (MLLMs), demonstrating their remarkable capabilities in understanding and reasoning across diverse tasks. However, these models are often trained fo
Externí odkaz:
http://arxiv.org/abs/2408.02503