
Tempura: A General Cost Based Optimizer Framework for Incremental Data Processing (Extended Version)
Incremental processing is widelyadopted in many applications, ranging f...
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Themes Informed Audiovisual Correspondence Learning
The applications of shortterm usergenerated 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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PoseGuided HighResolution Appearance Transfer via Progressive Training
We propose a novel poseguided appearance transfer network for transferr...
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APMSqueeze: A Communication Efficient AdamPreconditioned Momentum SGD Algorithm
Adam is the important optimization algorithm to guarantee efficiency and...
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GAN Slimming: AllinOne 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 Reparameterization
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 OffPolicy TDLearning
We present a novel l_1 regularized offpolicy convergent TDlearning 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 FiniteTime 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 CrossModal ImageText Retrieval
Enabling bidirectional retrieval of images and texts is important for u...
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Endtoend Robustness for SensingReasoning Machine Learning Pipelines
As machine learning (ML) being applied to many missioncritical scenario...
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MKPipe: A Compiler Framework for Optimizing MultiKernel Workloads in OpenCL for FPGA
OpenCL for FPGA enables developers to design FPGAs using a programming m...
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A novel treestructured 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 NonConvex Compositional Optimization
Stochastic compositional optimization arises in many important machine l...
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ATZSL: Defensive ZeroShot Recognition in the Presence of Adversaries
Zeroshot learning (ZSL) has received extensive attention recently espec...
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Hierarchical Prototype Learning for ZeroShot Recognition
ZeroShot 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 Singlesided 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 ErrorCompensated Compression
Communication is a key bottleneck in distributed training. Recently, an ...
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DeepSqueeze: Parallel Stochastic Gradient Descent with DoublePass ErrorCompensated Compression
Communication is a key bottleneck in distributed training. Recently, an ...
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Stochastic Convergence Results for Regularized ActorCritic Methods
In this paper, we present a stochastic convergence proof, under suitable...
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A CommunicationEfficient MultiAgent ActorCritic 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 DoublePass ErrorCompensated Compression
A standard approach in large scale machine learning is distributed stoch...
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A MultiAgent OffPolicy ActorCritic Algorithm for Distributed Reinforcement Learning
This paper extends offpolicy reinforcement learning to the multiagent ...
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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 SupportConfidenceaware 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: EnergyConstrained Deep Neural Network Compression via a Bilinear Regression Model
Many DNNenabled 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 PrimalDual Method for Empirical Risk Minimization with O(1) PerIteration 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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PMCGS: Parallel Monte Carlo Acyclic Graph Search
Recently, there have been great interests in Monte Carlo Tree Search (MC...
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Distributed Learning over Unreliable Networks
Most of today's distributed machine learning systems assume reliable ne...
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Revisit Batch Normalization: New Understanding from an Optimization View and a Refinement via Composition Optimization
Batch Normalization (BN) has been used extensively in deep learning to a...
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Parametrized Deep QNetworks Learning: Reinforcement Learning with DiscreteContinuous Hybrid Action Space
Most existing deep reinforcement learning (DRL) frameworks consider eith...
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Proximal Online Gradient is Optimum for Dynamic Regret
In online learning, the dynamic regret metric chooses the reference (opt...
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Fully Implicit Online Learning
Regularized online learning is widely used in machine learning. In this ...
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