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Found 18 papers, 11 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Federated Reinforcement Learning for Resource Allocation in V2X Networks
In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL).
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing
Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process.
Generalized Lightness Adaptation with Channel Selective Normalization
Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability.
Learning Fine-Grained Features for Pixel-wise Video Correspondences
We address the problem of learning features for establishing pixel-wise correspondences.
Learning Cross-Representation Affinity Consistency for Sparsely Supervised Biomedical Instance Segmentation
Sparse instance-level supervision has recently been explored to address insufficient annotation in biomedical instance segmentation, which is easier to annotate crowded instances and better preserves instance completeness for 3D volumetric datasets compared to common semi-supervision. In this paper, we propose a sparsely supervised biomedical instance segmentation framework via cross-representation affinity consistency regularization.
Exact Penalty Method for Federated Learning
Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics.
0/1 Deep Neural Networks via Block Coordinate Descent
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs).
Test-time Batch Normalization
Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift.
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge.
Federated Learning via Inexact ADMM
One of the crucial issues in federated learning is how to develop efficient optimization algorithms.
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast.
Accretionary Learning with Deep Neural Networks
In this paper, we propose a new learning method named Accretionary Learning (AL) to emulate human learning, in that the set of objects to be recognized may not be pre-specified.
Communication-Efficient ADMM-based Federated Learning
Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge.
Support Vector Machine Classifier via $L_{0/1}$ Soft-Margin Loss
To distinguish all of them, in this paper, we introduce a new model equipped with an $L_{0/1}$ soft-margin loss (dubbed as $L_{0/1}$-SVM) which well captures the nature of the binary classification.
Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit
Algorithms based on the hard thresholding principle have been well studied with sounding theoretical guarantees in the compressed sensing and more general sparsity-constrained optimization.
Optimization and Control
Sparse and Low-Rank Covariance Matrices Estimation
This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices.
