131 citations · 866 across the 39 of their papers we have counts for
17 papers · 1 filter
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…
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning
Ruijie Wang, Zheng Li, Jingfeng Yang +4
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages b…
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…
Phy-Taylor: Physics-Model-Based Deep Neural Networks
Yanbing Mao, Lui Sha, Huajie Shao +3
Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To ad…
Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
Dongxin Liu, Peng Wang, Tianshi Wang +1
This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data w…
Controllable and Diverse Text Generation in E-commerce
Huajie Shao, Jun Wang, Haohong Lin +4
In E-commerce, a key challenge in text generation is to find a good trade-off between word diversity and accuracy (relevance) in order to make generated text appear more natural an…
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…
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
Huajie Shao, Haohong Lin, Qinmin Yang +3
This paper challenges the common assumption that the weight , in -VAE, should be larger than in order to effectively disentangle latent factors. We demonstrate that -V…
Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion
Chaoqi Yang, Ruijie Wang, Shuochao Yao +1
Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in inform…
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…
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…
RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations
Shuochao Yao, Yiran Zhao, Huajie Shao +4
Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ub…
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
Shuochao Yao, Yiran Zhao, Aston Zhang +2
Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an imp…
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Shuochao Yao, Shaohan Hu, Yiran Zhao +2
Mobile sensing applications usually require time-series inputs from sensors. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate disp…