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08/01/2023
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment
Video quality assessment (VQA) has attracted growing attention in recent...
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07/31/2023
Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment
Video Quality Assessment (VQA), which aims to predict the perceptual qua...
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07/27/2023
Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
Recent advancements in surgical computer vision applications have been d...
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07/19/2023
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhance...
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06/28/2023
Momentum Benefits Non-IID Federated Learning Simply and Provably
Federated learning is a powerful paradigm for large-scale machine learni...
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06/01/2023
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm
Decentralized Stochastic Gradient Descent (SGD) is an emerging neural ne...
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05/25/2023
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
Communication compression is a common technique in distributed optimizat...
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05/12/2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
Communication compression is an essential strategy for alleviating commu...
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04/13/2023
Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment
Video quality assessment (VQA) aims to simulate the human perception of ...
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03/15/2023
BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection
While most recent autonomous driving system focuses on developing percep...
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03/01/2023
Quality-aware Pre-trained Models for Blind Image Quality Assessment
Blind image quality assessment (BIQA) aims to automatically evaluate the...
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02/13/2023
CholecTriplet2022: Show me a tool and tell me the triplet – an endoscopic vision challenge for surgical action triplet detection
Formalizing surgical activities as triplets of the used instruments, act...
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11/01/2022
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks
Decentralized optimization with time-varying networks is an emerging par...
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10/14/2022
Communication-Efficient Topologies for Decentralized Learning with O(1) Consensus Rate
Decentralized optimization is an emerging paradigm in distributed learni...
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10/14/2022
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Decentralized optimization is effective to save communication in large-s...
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10/10/2022
On the Performance of Gradient Tracking with Local Updates
We study the decentralized optimization problem where a network of n age...
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06/08/2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Recent advances in distributed optimization and learning have shown that...
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05/13/2022
Heavy-Tail Phenomenon in Decentralized SGD
Recent theoretical studies have shown that heavy-tails can emerge in sto...
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04/06/2022
ShowFace: Coordinated Face Inpainting with Memory-Disentangled Refinement Networks
Face inpainting aims to complete the corrupted regions of the face image...
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11/26/2021
Self-supervised Correlation Mining Network for Person Image Generation
Person image generation aims to perform non-rigid deformation on source ...
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11/08/2021
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
Decentralized algorithm is a form of computation that achieves a global ...
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10/26/2021
Exponential Graph is Provably Efficient for Decentralized Deep Training
Decentralized SGD is an emerging training method for deep learning known...
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10/19/2021
A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning
We study the consensus decentralized optimization problem where the obje...
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08/10/2021
Decentralized Composite Optimization with Compression
Decentralized optimization and communication compression have exhibited ...
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05/19/2021
Accelerating Gossip SGD with Periodic Global Averaging
Communication overhead hinders the scalability of large-scale distribute...
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05/17/2021
Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD
We consider decentralized stochastic optimization problems where a netwo...
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04/25/2021
On the Comparison between Cyclic Sampling and Random Reshuffling
When applying a stochastic/incremental algorithm, one must choose the or...
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04/24/2021
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training
The scale of deep learning nowadays calls for efficient distributed trai...
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03/30/2021
Differentiable Network Adaption with Elastic Search Space
In this paper we propose a novel network adaption method called Differen...
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03/22/2021
Incorporating Convolution Designs into Visual Transformers
Motivated by the success of Transformers in natural language processing ...
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02/08/2021
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
Sparsity in Deep Neural Networks (DNNs) has been widely studied to compr...
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10/02/2020
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks
One practice of employing deep neural networks is to apply the same arch...
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08/19/2020
Learning Connectivity of Neural Networks from a Topological Perspective
Seeking effective neural networks is a critical and practical field in d...
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05/26/2019
ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems and GANs
Despite remarkable empirical success, the training dynamics of generativ...
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05/20/2019
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
Decentralized optimization is a promising paradigm that finds various ap...
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03/26/2019
On the Performance of Exact Diffusion over Adaptive Networks
Various bias-correction methods such as EXTRA, DIGing, and exact diffusi...
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10/21/2018
Dynamic Average Diffusion with randomized Coordinate Updates
This work derives and analyzes an online learning strategy for tracking ...
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10/17/2018
Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates
This work develops a fully decentralized multi-agent algorithm for polic...
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10/17/2018
Multi-Agent Fully Decentralized Off-Policy Learning with Linear Convergence Rates
In this paper we develop a fully decentralized algorithm for policy eval...
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05/29/2018
Learning Under Distributed Features
This work studies the problem of learning under both large data and larg...
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04/18/2018
Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization
This paper addresses consensus optimization problems in a multi-agent ne...
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04/18/2018
A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization
This paper addresses consensus optimization problem in a multi-agent net...
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03/21/2018
Stochastic Learning under Random Reshuffling
In empirical risk optimization, it has been observed that stochastic gra...
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08/04/2017
Efficient Variance-Reduced Learning for Fully Decentralized On-Device Intelligence
This work develops a fully decentralized variance-reduced learning algor...
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08/04/2017
Convergence of Variance-Reduced Stochastic Learning under Random Reshuffling
Several useful variance-reduced stochastic gradient algorithms, such as ...
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03/14/2016
On the Influence of Momentum Acceleration on Online Learning
The article examines in some detail the convergence rate and mean-square...
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02/24/2016
