163 citations · 1.2k across the 51 of their papers we have counts for
51 papers
WiSparse: Boosting LLM Inference Efficiency with Weight-Aware Mixed Activation Sparsity
Lei Chen, Yuan Meng, Xiaoyu Zhan +2
Large Language Models (LLMs) offer strong capabilities but incur high inference costs due to dense computation and memory access. Training-free activation sparsity is a promising a…
Grounding Large Language Models as Generalizable Policies in Network Control
Duo Wu, Linjia Kang, Zhimin Wang +9
Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure. Yet network cont…
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
Chen Tang, Kai Ouyang, Zenghao Chai +4
Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation i.e., the policy) for each layer, especially when using lar…
Domain Generalization through the Lens of Angular Invariance
Yujie Jin, Xu Chu, Yasha Wang +1
Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift. A common pervasive theme in existing D…
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance
Chen Tang, Kai Ouyang, Zhi Wang +4
The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort t…
Fully Self-Supervised Learning for Semantic Segmentation
Yuan Wang, Wei Zhuo, Yucong Li +3
In this work, we present a fully self-supervised framework for semantic segmentation(FS^4). A fully bootstrapped strategy for semantic segmentation, which saves efforts for the hug…
Out-Of-Distribution Generalization on Graphs: A Survey
Haoyang Li, Xin Wang, Ziwei Zhang +1
Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is…
Self-directed Machine Learning
Wenwu Zhu, Xin Wang, Pengtao Xie
Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation met…
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
Xin Wang, Ziwei Zhang, Haoyang Li +1
Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and tec…
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
Haoyang Li, Xin Wang, Ziwei Zhang +1
Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution. However, existing GNNs lack out-of-distribu…
Controllable Video Captioning with an Exemplar Sentence
Yitian Yuan, Lin Ma, Jingwen Wang +1
In this paper, we investigate a novel and challenging task, namely controllable video captioning with an exemplar sentence. Formally, given a video and a syntactically valid exempl…
Syntax Customized Video Captioning by Imitating Exemplar Sentences
Yitian Yuan, Lin Ma, Wenwu Zhu
Enhancing the diversity of sentences to describe video contents is an important problem arising in recent video captioning research. In this paper, we explore this problem from a n…
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
A Survey on Temporal Sentence Grounding in Videos
Xiaohan Lan, Yitian Yuan, Xin Wang +2
Temporal sentence grounding in videos(TSGV), which aims to localize one target segment from an untrimmed video with respect to a given sentence query, has drawn increasing attentio…
Online Continual Adaptation with Active Self-Training
Shiji Zhou, Han Zhao, Shanghang Zhang +4
Models trained with offline data often suffer from continual distribution shifts and expensive labeling in changing environments. This calls for a new online learning paradigm wher…
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
Heng Chang, Yu Rong, Tingyang Xu +6
With the success of the graph embedding model in both academic and industry areas, the robustness of graph embedding against adversarial attack inevitably becomes a crucial problem…
AutoGL: A Library for Automated Graph Learning
Ziwei Zhang, Yijian Qin, Zeyang Zhang +11
Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for…
Automated Machine Learning on Graphs: A Survey
Ziwei Zhang, Xin Wang, Wenwu Zhu
Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and…
MetaDelta: A Meta-Learning System for Few-shot Image Classification
Yudong Chen, Chaoyu Guan, Zhikun Wei +2
Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks. Naturally, few-shot learning has been one of…
A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric
Yitian Yuan, Xiaohan Lan, Xin Wang +3
Temporal Sentence Grounding in Videos (TSGV), i.e., grounding a natural language sentence which indicates complex human activities in a long and untrimmed video sequence, has recei…
STSIR: Spatial Temporal Pandemic Model with Mobility Data
Wang Pan, Qipu Deng, Jiadong Li +2
With the outbreak of COVID-19, how to mitigate and suppress its spread is a big issue to the government. Department of public health need powerful models to model and predict the t…
A Survey on Curriculum Learning
Xin Wang, Yudong Chen, Wenwu Zhu
Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricul…
Implicit Graph Neural Networks
Fangda Gu, Heng Chang, Wenwu Zhu +2
Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data. Due to the finite nature of the underlying recur…
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
Ziwei Zhang, Chenhao Niu, Peng Cui +3
Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs…