131 citations · 423 across the 16 of their papers we have counts for
16 papers
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
Tianshi Wang, Jinyang Li, Ruijie Wang +7
This paper introduces SudokuSens, a generative framework for automated generation of training data in machine-learning-based Internet-of-Things (IoT) applications, such that the ge…
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
Shengzhong Liu, Tomoyoshi Kimura, Dongxin Liu +5
This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals through self-supervised…
DeepStream: Bandwidth Efficient Multi-Camera Video Streaming for Deep Learning Analytics
Hongpeng Guo, Beitong Tian, Zhe Yang +5
Deep learning video analytic systems process live video feeds from multiple cameras with computer vision models deployed on edge or cloud. To optimize utility for these systems, wh…
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
Ruijie Wang, Baoyu Li, Yichen Lu +6
This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both \textit{false negative issue} (i.e., potential true facts being excluded) and \t…
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs
Ruijie Wang, Zheng Li, Dachun Sun +4
In this paper, we investigate a realistic but underexplored problem, called few-shot temporal knowledge graph reasoning, that aims to predict future facts for newly emerging entiti…
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving
Jiyang Chen, Simon Yu, Rohan Tabish +4
Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks. Typically, these networks perform object detection uniformly on the…
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Jinning Li, Huajie Shao, Dachun Sun +6
This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space a…
DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction
Ruijie Wang, Zijie Huang, Shengzhong Liu +7
This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff…
Scheduling Real-time Deep Learning Services as Imprecise Computations
Shuochao Yao, Yifan Hao, Yiran Zhao +6
The paper presents an efficient real-time scheduling algorithm for intelligent real-time edge services, defined as those that perform machine intelligence tasks, such as voice reco…
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
Huajie Shao, Zhisheng Xiao, Shuochao Yao +3
This paper reviews the novel concept of controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic proper…
paper2repo: GitHub Repository Recommendation for Academic Papers
Huajie Shao, Dachun Sun, Jiahao Wu +7
GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release r…
ControlVAE: Controllable Variational Autoencoder
Huajie Shao, Shuochao Yao, Dachun Sun +5
Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation le…
Revisiting Over-smoothing in Deep GCNs
Chaoqi Yang, Ruijie Wang, Shuochao Yao +2
Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs). In this paper, we propose a new view that deep GCNs can actual…
Hierarchical Overlapping Belief Estimation by Structured Matrix Factorization
Chaoqi Yang, Jinyang Li, Ruijie Wang +6
Much work on social media opinion polarization focuses on a flat categorization of stances (or orthogonal beliefs) of different communities from media traces. We extend in this wor…
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
Shuochao Yao, Ailing Piao, Wenjun Jiang +10
Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human…
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Shuochao Yao, Yiran Zhao, Huajie Shao +4
Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impedi…