38 citations · 120 across the 24 of their papers we have counts for
24 papers
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
Yifei Shen, Jiawei Shao, Xinjie Zhang +5
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper…
Biological Factor Regulatory Neural Network
Xinnan Dai, Caihua Shan, Jie Zheng +2
Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their…
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies
Peijie Dong, Xin Niu, Lujun Li +5
Neural architecture search (NAS) has made tremendous progress in the automatic design of effective neural network structures but suffers from a heavy computational burden. One-shot…
Learning Domain Invariant Prompt for Vision-Language Models
Cairong Zhao, Yubin Wang, Xinyang Jiang +4
Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vecto…
Similarity Distribution based Membership Inference Attack on Person Re-identification
Junyao Gao, Xinyang Jiang, Huishuai Zhang +6
While person Re-identification (Re-ID) has progressed rapidly due to its wide real-world applications, it also causes severe risks of leaking personal information from training dat…
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
Wenli Sun, Xinyang Jiang, Shuguang Dou +4
In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, w…
Online Video Super-Resolution with Convolutional Kernel Bypass Graft
Jun Xiao, Xinyang Jiang, Ningxin Zheng +5
Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) in recent years, but most of these models are less applicable to online video applic…
Towards Privacy-Preserving Person Re-identification via Person Identify Shift
Shuguang Dou, Xinyang Jiang, Qingsong Zhao +2
Recently privacy concerns of person re-identification (ReID) raise more and more attention and preserving the privacy of the pedestrian images used by ReID methods become essential…
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One
Yezhen Wang, Tong Che, Bo Li +4
Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the in…
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering
Ruoxi Shi, Xinyang Jiang, Caihua Shan +2
Vector graphics (VG) have been ubiquitous in our daily life with vast applications in engineering, architecture, designs, etc. The VG recognition process of most existing methods i…
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng +4
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily.…
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling
Kaitao Song, Yichong Leng, Xu Tan +3
Sentence scoring aims at measuring the likelihood score of a sentence and is widely used in many natural language processing scenarios, like reranking, which is to select the best…
CMMD: Cross-Metric Multi-Dimensional Root Cause Analysis
Shifu Yan, Caihua Shan, Wenyi Yang +5
In large-scale online services, crucial metrics, a.k.a., key performance indicators (KPIs), are monitored periodically to check their running statuses. Generally, KPIs are aggregat…
Domain Generalization using Pretrained Models without Fine-tuning
Ziyue Li, Kan Ren, Xinyang Jiang +3
Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks. However, fine-tuning is usually computationally expensive due to the ever-growing size of pr…
Neural Piecewise-Constant Delay Differential Equations
Qunxi Zhu, Yifei Shen, Dongsheng Li +1
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data…
Recognizing Vector Graphics without Rasterization
Xinyang Jiang, Lu Liu, Caihua Shan +3
In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be sc…
Towards Generating Real-World Time Series Data
Hengzhi Pei, Kan Ren, Yuqing Yang +3
Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usua…
How Powerful is Graph Convolution for Recommendation?
Yifei Shen, Yongji Wu, Yao Zhang +4
Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empir…
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Yezhen Wang, Bo Li, Tong Che +3
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks ar…
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
Bo Li, Xinyang Jiang, Donglin Bai +6
The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of…
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear…
Decentralized Statistical Inference with Unrolled Graph Neural Networks
He Wang, Yifei Shen, Ziyuan Wang +4
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples with…
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning
Yiqin Yu, Xu Min, Shiwan Zhao +5
In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions. At the s…
Decentralized Markov Chain Gradient Descent
Tao Sun, Dongsheng Li
Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradie…