Zobrazeno 1 - 10
of 1 187
pro vyhledávání: '"Liu Yiqun"'
Autor:
Liu, Xiaolong, Zeng, Zhichen, Liu, Xiaoyi, Yuan, Siyang, Song, Weinan, Hang, Mengyue, Liu, Yiqun, Yang, Chaofei, Kim, Donghyun, Chen, Wen-Yen, Yang, Jiyan, Han, Yiping, Jin, Rong, Long, Bo, Tong, Hanghang, Yu, Philip S.
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into large-scale recommendation models. However, simply increasing the mode
Externí odkaz:
http://arxiv.org/abs/2411.13700
Autor:
Zeng, Zhichen, Liu, Xiaolong, Hang, Mengyue, Liu, Xiaoyi, Zhou, Qinghai, Yang, Chaofei, Liu, Yiqun, Ruan, Yichen, Chen, Laming, Chen, Yuxin, Hao, Yujia, Xu, Jiaqi, Nie, Jade, Liu, Xi, Zhang, Buyun, Wen, Wei, Yuan, Siyang, Wang, Kai, Chen, Wen-Yen, Han, Yiping, Li, Huayu, Yang, Chunzhi, Long, Bo, Yu, Philip S., Tong, Hanghang, Yang, Jiyan
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interest
Externí odkaz:
http://arxiv.org/abs/2411.09852
MA^2: A Self-Supervised and Motion Augmenting Autoencoder for Gait-Based Automatic Disease Detection
Ground reaction force (GRF) is the force exerted by the ground on a body in contact with it. GRF-based automatic disease detection (ADD) has become an emerging medical diagnosis method, which aims to learn and identify disease patterns corresponding
Externí odkaz:
http://arxiv.org/abs/2411.03129
Autor:
He, Yun, Chen, Xuxing, Xu, Jiayi, Cai, Renqin, You, Yiling, Cao, Jennifer, Huang, Minhui, Yang, Liu, Liu, Yiqun, Liu, Xiaoyi, Jin, Rong, Park, Sem, Long, Bo, Feng, Xue
In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources and improve recommendation performance via knowledge transfer between th
Externí odkaz:
http://arxiv.org/abs/2411.11871
The use of large language models (LLMs) as automated evaluation tools to assess the quality of generated natural language, known as LLMs-as-Judges, has demonstrated promising capabilities and is rapidly gaining widespread attention. However, when app
Externí odkaz:
http://arxiv.org/abs/2410.15393
Autor:
Chen, Junjie, Su, Weihang, Chu, Zhumin, Li, Haitao, Ai, Qinyao, Liu, Yiqun, Zhang, Min, Ma, Shaoping
With the rapid development of large language models (LLMs), how to efficiently evaluate them has become an important research question. Existing evaluation methods often suffer from high costs, limited test formats, the need of human references, and
Externí odkaz:
http://arxiv.org/abs/2410.12265
Autor:
Ye, Ziyi, Li, Xiangsheng, Li, Qiuchi, Ai, Qingyao, Zhou, Yujia, Shen, Wei, Yan, Dong, Liu, Yiqun
Learning from preference feedback is a common practice for aligning large language models~(LLMs) with human value. Conventionally, preference data is learned and encoded into a scalar reward model that connects a value head with an LLM to produce a s
Externí odkaz:
http://arxiv.org/abs/2410.03742
Large language models (LLMs) have made significant progress in natural language processing tasks and demonstrate considerable potential in the legal domain. However, legal applications demand high standards of accuracy, reliability, and fairness. App
Externí odkaz:
http://arxiv.org/abs/2409.20288
With exceptionally low magnetic damping, YIG has been extensively applied in the realm of magnetism, encompassing the researches into the spin Seebeck effect. YIG has 20 magnon bands, including 8 higher-energy bands denoted as $\alpha_{1\sim8}$, and
Externí odkaz:
http://arxiv.org/abs/2408.10058
Autor:
Rangadurai, Kaushik, Yuan, Siyang, Huang, Minhui, Liu, Yiqun, Ghasemiesfeh, Golnaz, Pu, Yunchen, Xie, Xinfeng, He, Xingfeng, Xu, Fangzhou, Cui, Andrew, Viswanathan, Vidhoon, Dong, Yan, Xiong, Liang, Yang, Lin, Wang, Liang, Yang, Jiyan, Sun, Chonglin
Embedding Based Retrieval (EBR) is a crucial component of the retrieval stage in (Ads) Recommendation System that utilizes Two Tower or Siamese Networks to learn embeddings for both users and items (ads). It then employs an Approximate Nearest Neighb
Externí odkaz:
http://arxiv.org/abs/2408.06653