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C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak
The novel coronavirus disease (COVID-19) has crushed daily routines and ...
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Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits
In this paper, we propose a novel quantum compiler optimization, named r...
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Federated Bandit: A Gossiping Approach
In this paper, we study Federated Bandit, a decentralized Multi-Armed Ba...
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Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach
With the rising of short video apps, such as TikTok, Snapchat and Kwai, ...
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Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Adversarial training and its many variants substantially improve deep ne...
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Tempura: A General Cost Based Optimizer Framework for Incremental Data Processing (Extended Version)
Incremental processing is widely-adopted in many applications, ranging f...
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Themes Informed Audio-visual Correspondence Learning
The applications of short-term user-generated video (UGV), such as Snapc...
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ServiceNet: A P2P Service Network
Given a large number of online services on the Internet, from time to ti...
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Pose-Guided High-Resolution Appearance Transfer via Progressive Training
We propose a novel pose-guided appearance transfer network for transferr...
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APMSqueeze: A Communication Efficient Adam-Preconditioned Momentum SGD Algorithm
Adam is the important optimization algorithm to guarantee efficiency and...
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GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Generative adversarial networks (GANs) have gained increasing popularity...
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Streaming Probabilistic Deep Tensor Factorization
Despite the success of existing tensor factorization methods, most of th...
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Lossless CNN Channel Pruning via Gradient Resetting and Convolutional Re-parameterization
Channel pruning (a.k.a. filter pruning) aims to slim down a convolutiona...
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On Effective Parallelization of Monte Carlo Tree Search
Despite its groundbreaking success in Go and computer games, Monte Carlo...
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Neural Network Activation Quantization with Bitwise Information Bottlenecks
Recent researches on information bottleneck shed new light on the contin...
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Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
In this paper, we introduce proximal gradient temporal difference learni...
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Regularized Off-Policy TD-Learning
We present a novel l_1 regularized off-policy convergent TD-learning met...
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Data Poisoning Attacks on Federated Machine Learning
Federated machine learning which enables resource constrained node devic...
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A Private and Finite-Time Algorithm for Solving a Distributed System of Linear Equations
This paper studies a system of linear equations, denoted as Ax = b, whic...
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Depth Edge Guided CNNs for Sparse Depth Upsampling
Guided sparse depth upsampling aims to upsample an irregularly sampled s...
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Stochastic Recursive Momentum for Policy Gradient Methods
In this paper, we propose a novel algorithm named STOchastic Recursive M...
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IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Enabling bi-directional retrieval of images and texts is important for u...
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End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines
As machine learning (ML) being applied to many mission-critical scenario...
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MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA
OpenCL for FPGA enables developers to design FPGAs using a programming m...
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A novel tree-structured point cloud dataset for skeletonization algorithm evaluation
Curve skeleton extraction from unorganized point cloud is a fundamental ...
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Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization
Stochastic compositional optimization arises in many important machine l...
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ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries
Zero-shot learning (ZSL) has received extensive attention recently espec...
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Hierarchical Prototype Learning for Zero-Shot Recognition
Zero-Shot Learning (ZSL) has received extensive attention and successes ...
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Learning Sparsity and Quantization Jointly and Automatically for Neural Network Compression via Constrained Optimization
Deep Neural Networks (DNNs) are widely applied in a wide range of usecas...
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Central Server Free Federated Learning over Single-sided Trust Social Networks
Federated learning has become increasingly important for modern machine ...
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An Interactive Control Approach to 3D Shape Reconstruction
The ability to accurately reconstruct the 3D facets of a scene is one of...
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Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Deep Neural Network (DNN) is powerful but computationally expensive and ...
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PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Graph node embedding aims at learning a vector representation for all no...
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DeepSqueeze: Decentralization Meets Error-Compensated Compression
Communication is a key bottleneck in distributed training. Recently, an ...
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DeepSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
Communication is a key bottleneck in distributed training. Recently, an ...
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Stochastic Convergence Results for Regularized Actor-Critic Methods
In this paper, we present a stochastic convergence proof, under suitable...
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A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning
This paper considers a distributed reinforcement learning problem in whi...
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DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
A standard approach in large scale machine learning is distributed stoch...
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A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning
This paper extends off-policy reinforcement learning to the multi-agent ...
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Optimal Projection Guided Transfer Hashing for Image Retrieval
Recently, learning to hash has been widely studied for image retrieval t...
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SCEF: A Support-Confidence-aware Embedding Framework for Knowledge Graph Refinement
Knowledge graph (KG) refinement mainly aims at KG completion and correct...
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Adversarially Trained Model Compression: When Robustness Meets Efficiency
The robustness of deep models to adversarial attacks has gained signific...
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Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend
Decentralized Online Learning (online learning in decentralized networks...
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Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis
For recovering 3D object poses from 2D images, a prevalent method is to ...
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Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks
Recently, an opinion dynamics model has been proposed to describe a netw...
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ECC: Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model
Many DNN-enabled vision applications constantly operate under severe ene...
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Distributed Learning of Average Belief Over Networks Using Sequential Observations
This paper addresses the problem of distributed learning of average beli...
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Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity
Regularized empirical risk minimization problem with linear predictor ap...
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Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning
Dantzig Selector (DS) is widely used in compressed sensing and sparse le...
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P-MCGS: Parallel Monte Carlo Acyclic Graph Search
Recently, there have been great interests in Monte Carlo Tree Search (MC...
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