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Latent Dirichlet Allocation Model Training with Differential Privacy
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique ...
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A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
Spherical videos, also known as 360 (panorama) videos, can be viewed wit...
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Video Super Resolution Based on Deep Learning: A comprehensive survey
In recent years, deep learning has made great progress in the fields of ...
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Storage Fit Learning with Feature Evolvable Streams
Feature evolvable learning has been widely studied in recent years where...
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Dynamic Regret of Convex and Smooth Functions
We investigate online convex optimization in non-stationary environments...
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Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions
In this paper, we present an improved analysis for dynamic regret of str...
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CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning
The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims a...
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Bayesian Spatial Homogeneity Pursuit Regression for Count Value Data
Spatial regression models are ubiquitous in many different areas such as...
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Exploratory Machine Learning with Unknown Unknowns
In conventional supervised learning, a training dataset is given with gr...
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An Unbiased Risk Estimator for Learning with Augmented Classes
In this paper, we study the problem of learning with augmented classes (...
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Bandit Convex Optimization in Non-stationary Environments
Bandit Convex Optimization (BCO) is a fundamental framework for modeling...
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Implicit Regularization via Hadamard Product Over-Parametrization in High-Dimensional Linear Regression
We consider Hadamard product parametrization as a change-of-variable (ov...
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Background subtraction on depth videos with convolutional neural networks
Background subtraction is a significant component of computer vision sys...
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Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
Recently, deep neural networks (DNNs) have been widely applied in mobile...
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Handling Concept Drift via Model Reuse
In many real-world applications, data are often collected in the form of...
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Distribution-Free One-Pass Learning
In many large-scale machine learning applications, data are accumulated ...
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