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Disentangled Recurrent Wasserstein Autoencoder
Learning disentangled representations leads to interpretable models and ...
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Heuristic-based Mining of Service Behavioral Models from Interaction Traces
Software behavioral models have proven useful for emulating and testing ...
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p-order Tensor Products with Invertible Linear Transforms
This paper studies the issues about tensors. Three typical kinds of tens...
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LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
The resolution of GPS measurements, especially in urban areas, is insuff...
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Scalable Approximate Inference and Some Applications
Approximate inference in probability models is a fundamental task in mac...
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Stein Variational Inference for Discrete Distributions
Gradient-based approximate inference methods, such as Stein variational ...
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SDSN@RT: a middleware environment for single-instance multi-tenant cloud applications
With the Single-Instance Multi-Tenancy (SIMT) model for composite Softwa...
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FM4SN: A Feature-Oriented Approach to Tenant-Driven Customization of Multi-Tenant Service Networks
In a multi-tenant service network, multiple virtual service networks (VS...
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CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data
The electronic calendar is a valuable resource nowadays for managing our...
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LINS: A Lidar-Inerital State Estimator for Robust and Fast Navigation
Robust and fast ego-motion estimation is a critical problem for autonomo...
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SurFi: Detecting Surveillance Camera Looping Attacks with Wi-Fi Channel State Information (Extended Version)
The proliferation of surveillance cameras has greatly improved the physi...
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ContextServ: Towards Model-Driven Development of Context-AwareWeb Services
In the era of Web of Things and Services, Context-aware Web Services (CA...
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Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior
Mobile phones can record individual's daily behavioral data as a time-se...
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Deep Probabilistic Video Compression
We propose a variational inference approach to deep probabilistic video ...
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Stein Variational Gradient Descent Without Gradient
Stein variational gradient decent (SVGD) has been shown to be a powerful...
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A hybrid architecture for astronomical computing
With many large science equipment constructing and putting into use, ast...
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Identifying Recent Behavioral Data Length in Mobile Phone Log
Mobile phone log data (e.g., phone call log) is not static as it is prog...
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An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior
The presence of noisy instances in mobile phone data is a fundamental is...
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Stein Variational Adaptive Importance Sampling
We propose a novel adaptive importance sampling algorithm which incorpor...
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Bootstrap Model Aggregation for Distributed Statistical Learning
In distributed, or privacy-preserving learning, we are often given a set...
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