39 citations · 125 across the 20 of their papers we have counts for
20 papers
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning
Ke Xu, Ziliang Wang, Wei Zheng +4
Cross-domain recommendation (CDR) methods are proposed to tackle the sparsity problem in click through rate (CTR) estimation. Existing CDR methods directly transfer knowledge from…
OWL: A Large Language Model for IT Operations
Hongcheng Guo, Jian Yang, Jiaheng Liu +13
With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques o…
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device
Weiran Gou, Ziyao Yi, Yan Xiang +4
With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possi…
EQ-Net: Elastic Quantization Neural Networks
Ke Xu, Lei Han, Ye Tian +2
Current model quantization methods have shown their promising capability in reducing storage space and computation complexity. However, due to the diversity of quantization forms s…
Open Data on GitHub: Unlocking the Potential of AI
Anthony Cintron Roman, Kevin Xu, Arfon Smith +3
GitHub is the world's largest platform for collaborative software development, with over 100 million users. GitHub is also used extensively for open data collaboration, hosting mor…
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
He Zhu, Chong Zhang, Junjie Huang +2
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in…
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
Junran Wu, Xueyuan Chen, Bowen Shi +2
In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contra…
Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach
Xingyu Peng, Zhenkun Zhou, Chong Zhang +1
Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions…
MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search
Xiaoyang Zheng, Zilong Wang, Ke Xu +4
Taobao Search consists of two phases: the retrieval phase and the ranking phase. Given a user query, the retrieval phase returns a subset of candidate products for the following ra…
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
Minghao Li, Ran Ben Basat, Shay Vargaftik +4
Deep neural networks (DNNs) are the de facto standard for essential use cases, such as image classification, computer vision, and natural language processing. As DNNs and datasets…
DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks
Chong Zhang, Zhenkun Zhou, Xingyu Peng +1
Given the development and abundance of social media, studying the stance of social media users is a challenging and pressing issue. Social media users express their stance by posti…
3D-QueryIS: A Query-based Framework for 3D Instance Segmentation
Jiaheng Liu, Tong He, Honghui Yang +6
Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness. Besides, inevitable variations of…
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Radu Timofte, Shuai Liu +35
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In thi…
Boosting Night-time Scene Parsing with Learnable Frequency
Zhifeng Xie, Sen Wang, Ke Xu +4
Night-Time Scene Parsing (NTSP) is essential to many vision applications, especially for autonomous driving. Most of the existing methods are proposed for day-time scene parsing. T…
Quantized Adaptive Subgradient Algorithms and Their Applications
Ke Xu, Jianqiao Wangni, Yifan Zhang +3
Data explosion and an increase in model size drive the remarkable advances in large-scale machine learning, but also make model training time-consuming and model storage difficult.…
Structural Entropy Guided Graph Hierarchical Pooling
Junran Wu, Xueyuan Chen, Ke Xu +1
Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of th…
CoupleFace: Relation Matters for Face Recognition Distillation
Jiaheng Liu, Haoyu Qin, Yichao Wu +3
Knowledge distillation is an effective method to improve the performance of a lightweight neural network (i.e., student model) by transferring the knowledge of a well-performed neu…
Rethinking Efficient Lane Detection via Curve Modeling
Zhengyang Feng, Shaohua Guo, Xin Tan +3
This paper presents a novel parametric curve-based method for lane detection in RGB images. Unlike state-of-the-art segmentation-based and point detection-based methods that typica…
Hierarchical information matters: Text classification via tree based graph neural network
Chong Zhang, He Zhu, Xingyu Peng +2
Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification ta…
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks
Tiancheng Huang, Ke Xu, Donglin Wang
Domain adaptation using graph-structured networks learns label-discriminative and network-invariant node embeddings by sharing graph parameters. Most existing works focus on domain…