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pro vyhledávání: '"Chen, Guangyi"'
Autor:
Sun, Yuewen, Kong, Lingjing, Chen, Guangyi, Li, Loka, Luo, Gongxu, Li, Zijian, Zhang, Yixuan, Zheng, Yujia, Yang, Mengyue, Stojanov, Petar, Segal, Eran, Xing, Eric P., Zhang, Kun
Prevalent in biological applications (e.g., human phenotype measurements), multimodal datasets can provide valuable insights into the underlying biological mechanisms. However, current machine learning models designed to analyze such datasets still l
Externí odkaz:
http://arxiv.org/abs/2411.06518
Autor:
Wu, Anpeng, Kuang, Kun, Zhu, Minqin, Wang, Yingrong, Zheng, Yujia, Han, Kairong, Li, Baohong, Chen, Guangyi, Wu, Fei, Zhang, Kun
Recent breakthroughs in artificial intelligence have driven a paradigm shift, where large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks.
Externí odkaz:
http://arxiv.org/abs/2410.15319
Autor:
Song, Xiangchen, Li, Zijian, Chen, Guangyi, Zheng, Yujia, Fan, Yewen, Dong, Xinshuai, Zhang, Kun
Causal Temporal Representation Learning (Ctrl) methods aim to identify the temporal causal dynamics of complex nonstationary temporal sequences. Despite the success of existing Ctrl methods, they require either directly observing the domain variables
Externí odkaz:
http://arxiv.org/abs/2409.03142
Autor:
Chen, Ce, Huang, Shaoli, Chen, Xuelin, Chen, Guangyi, Han, Xiaoguang, Zhang, Kun, Gong, Mingming
Text-to-4D generation has recently been demonstrated viable by integrating a 2D image diffusion model with a video diffusion model. However, existing models tend to produce results with inconsistent motions and geometric structures over time. To this
Externí odkaz:
http://arxiv.org/abs/2408.08342
Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset. Identifiability, as the central theme of this approach, normally hinges on leveraging data fr
Externí odkaz:
http://arxiv.org/abs/2408.05788
Autor:
Liu, Zuyan, Liu, Benlin, Wang, Jiahui, Dong, Yuhao, Chen, Guangyi, Rao, Yongming, Krishna, Ranjay, Lu, Jiwen
In the field of instruction-following large vision-language models (LVLMs), the efficient deployment of these models faces challenges, notably due to the high memory demands of their key-value (KV) caches. Conventional cache management strategies for
Externí odkaz:
http://arxiv.org/abs/2407.18121
Recent works have introduced GNN-to-MLP knowledge distillation (KD) frameworks to combine both GNN's superior performance and MLP's fast inference speed. However, existing KD frameworks are primarily designed for node classification within single gra
Externí odkaz:
http://arxiv.org/abs/2406.19832
To better interpret the intrinsic mechanism of large language models (LLMs), recent studies focus on monosemanticity on its basic units. A monosemantic neuron is dedicated to a single and specific concept, which forms a one-to-one correlation between
Externí odkaz:
http://arxiv.org/abs/2406.17969
Learning concepts from natural high-dimensional data (e.g., images) holds potential in building human-aligned and interpretable machine learning models. Despite its encouraging prospect, formalization and theoretical insights into this crucial task a
Externí odkaz:
http://arxiv.org/abs/2406.00519
Autor:
Cai, Ruichu, Jiang, Zhifang, Li, Zijian, Chen, Weilin, Chen, Xuexin, Hao, Zhifeng, Shen, Yifan, Chen, Guangyi, Zhang, Kun
Existing methods for multi-modal time series representation learning aim to disentangle the modality-shared and modality-specific latent variables. Although achieving notable performances on downstream tasks, they usually assume an orthogonal latent
Externí odkaz:
http://arxiv.org/abs/2405.16083