Zobrazeno 1 - 10
of 4 396
pro vyhledávání: '"LIU, Zhuang"'
Autor:
Shen, Xiaoqian, Xiong, Yunyang, Zhao, Changsheng, Wu, Lemeng, Chen, Jun, Zhu, Chenchen, Liu, Zechun, Xiao, Fanyi, Varadarajan, Balakrishnan, Bordes, Florian, Liu, Zhuang, Xu, Hu, Kim, Hyunwoo J., Soran, Bilge, Krishnamoorthi, Raghuraman, Elhoseiny, Mohamed, Chandra, Vikas
Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we pro
Externí odkaz:
http://arxiv.org/abs/2410.17434
Autor:
Li, Zeping, Yang, Xinlong, Gao, Ziheng, Liu, Ji, Li, Guanchen, Liu, Zhuang, Li, Dong, Peng, Jinzhang, Tian, Lu, Barsoum, Emad
Large Language Models (LLMs) inherently use autoregressive decoding, which lacks parallelism in inference and results in significantly slow inference speed. While methods such as Medusa constructs parallelized heads, they lack adequate information in
Externí odkaz:
http://arxiv.org/abs/2406.13170
Knowledge tracing (KT), aiming to mine students' mastery of knowledge by their exercise records and predict their performance on future test questions, is a critical task in educational assessment. While researchers achieved tremendous success with t
Externí odkaz:
http://arxiv.org/abs/2405.14391
Atmosphere modelling applications become increasingly memory-bound due to the inconsistent development rates between processor speeds and memory bandwidth. In this study, we mitigate memory bottlenecks and reduce the computational load of the GRIST d
Externí odkaz:
http://arxiv.org/abs/2404.08849
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space
Externí odkaz:
http://arxiv.org/abs/2404.05962
Autor:
Liu, Zhuang, He, Kaiming
We revisit the "dataset classification" experiment suggested by Torralba and Efros a decade ago, in the new era with large-scale, diverse, and hopefully less biased datasets as well as more capable neural network architectures. Surprisingly, we obser
Externí odkaz:
http://arxiv.org/abs/2403.08632
Autor:
Xiong, Zhang, Li, Haoxuan, Liu, Zhuang, Chen, Zhuofan, Zhou, Hao, Rong, Wenge, Ouyang, Yuanxin
Publikováno v:
Frontiers of Digital Education, 2024 ,1(1): 26-50
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights int
Externí odkaz:
http://arxiv.org/abs/2402.17236
We observe an empirical phenomenon in Large Language Models (LLMs) -- very few activations exhibit significantly larger values than others (e.g., 100,000 times larger). We call them massive activations. First, we demonstrate the widespread existence
Externí odkaz:
http://arxiv.org/abs/2402.17762
Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also \textit{generate high-performing neural network parameters}. Our approach is simple, utilizing an autoencoder
Externí odkaz:
http://arxiv.org/abs/2402.13144
Publikováno v:
Remote Sens. 2024, 16, 1653
Effectively and efficiently retrieving images from remote sensing databases is a critical challenge in the realm of remote sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the pote
Externí odkaz:
http://arxiv.org/abs/2402.02141