Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Jin, Yilun"'
Autor:
Jin, Yilun, Li, Zheng, Zhang, Chenwei, Cao, Tianyu, Gao, Yifan, Jayarao, Pratik, Li, Mao, Liu, Xin, Sarkhel, Ritesh, Tang, Xianfeng, Wang, Haodong, Wang, Zhengyang, Xu, Wenju, Yang, Jingfeng, Yin, Qingyu, Li, Xian, Nigam, Priyanka, Xu, Yi, Chen, Kai, Yang, Qiang, Jiang, Meng, Yin, Bing
Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full
Externí odkaz:
http://arxiv.org/abs/2410.20745
Due to high accuracy, BERT-like models have been widely adopted by discriminative text mining and web searching. However, large BERT-like models suffer from inefficient online inference, as they face the following two problems on GPUs. First, they re
Externí odkaz:
http://arxiv.org/abs/2408.12526
Autor:
Yang, Liu, Cai, Shuowei, Chai, Di, Zhang, Junxue, Tian, Han, Jin, Yilun, Guo, Kun, Chen, Kai, Yang, Qiang
As an essential tool of secure distributed machine learning, vertical federated learning (VFL) based on homomorphic encryption (HE) suffers from severe efficiency problems due to data inflation and time-consuming operations. To this core, we propose
Externí odkaz:
http://arxiv.org/abs/2405.00482
Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features among multiple parties. Most VFL algorithms primarily rely on two fun
Externí odkaz:
http://arxiv.org/abs/2306.16139
Autor:
Yang, Liu, Chai, Di, Zhang, Junxue, Jin, Yilun, Wang, Leye, Liu, Hao, Tian, Han, Xu, Qian, Chen, Kai
Vertical federated learning (VFL) is a promising category of federated learning for the scenario where data is vertically partitioned and distributed among parties. VFL enriches the description of samples using features from different parties to impr
Externí odkaz:
http://arxiv.org/abs/2304.01829
Data privacy has become an increasingly important concern in real-world big data applications such as machine learning. To address the problem, federated learning (FL) has been a promising solution to building effective machine learning models from d
Externí odkaz:
http://arxiv.org/abs/2303.14453
Autor:
Cai, Shuowei, Chai, Di, Yang, Liu, Zhang, Junxue, Jin, Yilun, Wang, Leye, Guo, Kun, Chen, Kai
Vertical federated learning (VFL) is attracting much attention because it enables cross-silo data cooperation in a privacy-preserving manner. While most research works in VFL focus on linear and tree models, deep models (e.g., neural networks) are no
Externí odkaz:
http://arxiv.org/abs/2207.00165
Publikováno v:
In Expert Systems With Applications 1 October 2024 251
Graph neural networks (GNNs) have achieved tremendous success in graph mining. However, the inability of GNNs to model substructures in graphs remains a significant drawback. Specifically, message-passing GNNs (MPGNNs), as the prevailing type of GNNs
Externí odkaz:
http://arxiv.org/abs/2104.02995
Autor:
Liu, Chang, Fan, Lixin, Ng, Kam Woh, Jin, Yilun, Ju, Ce, Zhang, Tianyu, Chan, Chee Seng, Yang, Qiang
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts. Two kinds of axiomatic t
Externí odkaz:
http://arxiv.org/abs/2103.09173