K-sparing
Pro Research Analysisby 
Searched over 200M research papers for "k-sparing"
Advances in k-Sparing Techniques in Various Domains
Global Top-k Sparsification in Distributed Synchronous SGD
Keywords: Global Top-k Sparsification, Distributed Synchronous SGD, Low Bandwidth Networks
Distributed synchronous stochastic gradient descent (S-SGD) is a popular method for training large-scale deep neural networks (DNNs). However, it demands high communication bandwidth to exchange gradients among computational workers. Top-k sparsification techniques have emerged to mitigate this issue by reducing the volume of data exchanged, effectively zeroing out a significant portion of gradients without affecting model convergence. A novel global Top-k (gTop-k) sparsification mechanism has been proposed to further optimize this process. Instead of accumulating all local top-k gradients, gTop-k selects the global top-k largest absolute gradient values from all workers, reducing communication complexity from O(kP) to O(k log P). This method, known as gTopKAllReduce, has shown to improve scaling efficiency significantly, achieving 2.7-12× higher efficiency compared to dense gradient S-SGD and 1.1-1.7× improvement over existing Top-k S-SGD methods1.
k-Sparse Autoencoders for Improved Classification
Keywords: k-Sparse Autoencoders, Classification Performance, Sparse Representations
k-Sparse autoencoders have been introduced to enhance classification performance by encouraging sparsity in learned representations. This method retains only the k highest activities in the hidden layers, which has been shown to outperform denoising autoencoders, networks trained with dropout, and Restricted Boltzmann Machines (RBMs) on datasets like MNIST and NORB. The simplicity and speed of training k-sparse autoencoders make them suitable for large problem sizes where traditional sparse coding algorithms are impractical2.
Compressed Sensing in MRI with Sparse Representations
Keywords: Compressed Sensing, MRI, Sparse Representations
Compressed sensing (CS) leverages the inherent sparsity in MR images to enable accurate reconstruction from undersampled k-space data. By employing sparsifying transforms such as wavelets and finite differences, CS can recover images from significantly undersampled data. This approach has demonstrated improved spatial resolution and accelerated acquisition in various MRI applications, including multislice fast spin-echo brain imaging and 3D contrast-enhanced angiography3. Additionally, the sparse k-t PCA algorithm enhances dynamic MRI by combining k-t PCA with an artificial sparsity constraint, improving temporal resolution and reducing reconstruction errors4.
Energy-Aware Scheduling with Standby-Sparing in Real-Time Systems
Keywords: Energy-Aware Scheduling, Standby-Sparing, Real-Time Systems
In real-time computing systems, energy efficiency, quality of service (QoS), and fault tolerance are critical design concerns. Standby-sparing systems, which use a primary and a spare processor, provide fault tolerance for both permanent and transient faults. Novel scheduling schemes have been proposed to enforce (m,k)-deadlines while reducing energy consumption. These schemes significantly outperform previous methods in energy conservation while ensuring fault tolerance and meeting QoS constraints5 7.
Potassium-Sparing Agents in Hypertension Management
Keywords: Potassium-Sparing Agents, Hypertension, Diuretic Therapy
Potassium-sparing agents are used in conjunction with diuretic therapy to manage hypertension and prevent potassium loss. Studies have shown that while triamterene may be ineffective at certain dosages, spironolactone and Slow-K can significantly reduce or completely reverse potassium deficits. Plasma potassium levels, however, may not accurately reflect the degree of potassium restoration achieved8.
Subspace Clustering with Entropy Weighting k-Means
Keywords: Subspace Clustering, High-Dimensional Data, Entropy Weighting k-Means
Clustering high-dimensional data often involves identifying clusters in subspaces rather than the entire space. An entropy weighting k-means algorithm has been developed to address this by calculating a weight for each dimension in each cluster. This method improves clustering results by focusing on important dimensions that categorize different clusters, demonstrating better performance and scalability compared to other subspace clustering algorithms9.
Enhancing K-SVD Denoising with Deep Learning
Keywords: K-SVD Denoising, Deep Learning, Image Processing
K-SVD denoising, a sparsity-based method for noise removal from images, has been revisited with a deep learning approach. By redesigning the algorithm to operate in a supervised manner, an end-to-end deep architecture with the K-SVD computational path has been developed. This new approach significantly outperforms the classical K-SVD algorithm and approaches the performance of recent state-of-the-art learning-based denoising methods, bridging the gap between traditional and modern techniques10.
Conclusion
The advancements in k-sparing techniques across various domains highlight the importance of sparsity in improving performance and efficiency. From distributed machine learning and autoencoders to MRI reconstruction and real-time systems, these methods offer significant benefits in terms of communication efficiency, classification accuracy, image quality, and energy conservation. As research continues, these techniques are likely to see broader applications and further refinements.
Sources and full results
Most relevant research papers on this topic
A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks
Our global Top-k (gTop-k) sparsification mechanism significantly improves the scaling efficiency of distributed synchronous stochastic gradient descent on low bandwidth networks, while maintaining consistent convergence performance.
Highly Citedk-Sparse Autoencoders
K-sparse autoencoders, which only retain the k highest activities in hidden layers, achieve better classification results than denoising autoencoders, networks trained with dropout, and RBMs.
Highly CitedSparse MRI: The application of compressed sensing for rapid MR imaging
Sparse MRI, using compressed sensing, improves spatial resolution and accelerates acquisition in multislice fast spin-echo brain imaging and 3D contrast enhanced angiography.
Highly CitedImproved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI
The sparse k-t PCA algorithm improves dynamic MRI performance by reducing reconstruction error and improving temporal resolution compared to the standard k-t PCA algorithm.
Reliable and Energy-Aware Fixed-Priority (m,k)-Deadlines Enforcement with Standby-Sparing
Our novel scheduling scheme significantly reduces energy consumption while maintaining (m,k)-deadlines and fault tolerance in real-time systems.
MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning
The adaptive dictionary learning framework significantly improves MR image reconstruction accuracy and undersampling rates compared to previous methods.
Highly CitedFixed-Priority Scheduling for Reliable and Energy-Aware (m, k)-Deadlines Enforcement With Standby-Sparing
Our novel scheduling schemes significantly reduce energy consumption while maintaining quality of service and fault tolerance in real-time systems.
Potassium-sparing Agents during Diuretic Therapy in Hypertension
Spironolactone reduces potassium loss during diuretic therapy, while Slow-K completely restores potassium deficits in hypertensive patients.
Non-RCTAn Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
The new k-means algorithm with weighted dimensions improves subspace clustering in high-dimensional data, generating better results and being scalable to large data sets.
Highly CitedDeep K-SVD Denoising
Deep K-SVD denoising, redesigned in a supervised manner, can substantially improve image noise removal compared to classical K-SVD methods.
Highly Cited