Zobrazeno 1 - 10
of 201
pro vyhledávání: '"Lu, Tun"'
Autor:
Wang, Chenyu, Yan, Shuo, Chen, Yixuan, Wang, Yujiang, Dong, Mingzhi, Yang, Xiaochen, Li, Dongsheng, Dick, Robert P., Lv, Qin, Yang, Fan, Lu, Tun, Gu, Ning, Shang, Li
Video generation using diffusion-based models is constrained by high computational costs due to the frame-wise iterative diffusion process. This work presents a Diffusion Reuse MOtion (Dr. Mo) network to accelerate latent video generation. Our key di
Externí odkaz:
http://arxiv.org/abs/2409.12532
Graph Neural Networks (GNNs) have demonstrated effectiveness in collaborative filtering tasks due to their ability to extract powerful structural features. However, combining the graph features extracted from user-item interactions and auxiliary feat
Externí odkaz:
http://arxiv.org/abs/2408.05792
Over the past few years, many efforts have been dedicated to studying cyberbullying in social edge computing devices, and most of them focus on three roles: victims, perpetrators, and bystanders. If we want to obtain a deep insight into the formation
Externí odkaz:
http://arxiv.org/abs/2408.03502
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as w
Externí odkaz:
http://arxiv.org/abs/2407.19937
Autor:
Shi, Yubin, Chen, Yixuan, Dong, Mingzhi, Yang, Xiaochen, Li, Dongsheng, Wang, Yujiang, Dick, Robert P., Lv, Qin, Zhao, Yingying, Yang, Fan, Lu, Tun, Gu, Ning, Shang, Li
Despite their prevalence in deep-learning communities, over-parameterized models convey high demands of computational costs for proper training. This work studies the fine-grained, modular-level learning dynamics of over-parameterized models to attai
Externí odkaz:
http://arxiv.org/abs/2405.07527
Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks. To steer LLMs towards human preference, alignment technologies have been introduced and gained increasing attention. Nevertheless, existing methods heav
Externí odkaz:
http://arxiv.org/abs/2403.03419
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the importance of various interactions that reflect unique user/item charact
Externí odkaz:
http://arxiv.org/abs/2402.08426
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks
Autor:
Lu, Yuxuan, Yao, Bingsheng, Zhang, Shao, Wang, Yun, Zhang, Peng, Lu, Tun, Li, Toby Jia-Jun, Wang, Dakuo
Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance. However, in real-world tasks, domain knowledge is often required. Low-resource learning methods like Acti
Externí odkaz:
http://arxiv.org/abs/2311.09825
Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the prediction performance of CTR models. However, existing methods face thr
Externí odkaz:
http://arxiv.org/abs/2311.04635
Click-through rate (CTR) prediction is widely used in academia and industry. Most CTR tasks fall into a feature embedding \& feature interaction paradigm, where the accuracy of CTR prediction is mainly improved by designing practical feature interact
Externí odkaz:
http://arxiv.org/abs/2311.04625