Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
As one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classification. DNN achieves such performance, to a larg…
Data CompressionGeneral Classificationimage-classificationImage Classification+13LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning
The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes. Overly-aggressive attempts to reduce communication often sacrifice final…
BIG-bench Machine LearningData CompressionGPUQuantizationSimple strategies for recovering inner products from coarsely quantized random projections
Random projections have been increasingly adopted for a diverse set of tasks in machine learning involving dimensionality reduction. One specific line of research on this topic has investigated the use of quantization su…
Data CompressionDimensionality ReductionQuantizationSemisupervised Clustering, AND-Queries and Locally Encodable Source Coding
Source coding is the canonical problem of data compression in information theory. In a locally encodable source coding, each compressed bit depends on only few bits of the input. In this paper, we show that a recently p…
ClusteringData CompressionCamera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression
We propose a novel lossless and lossy compression scheme for color filter array~(CFA) sampled images based on the wavelet transform of them. Our analysis suggests that the wavelet coefficients of HL and LH subbands are h…
Data CompressionText Compression for Sentiment Analysis via Evolutionary Algorithms
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), whic…
Data CompressionEvolutionary AlgorithmsGeneral ClassificationSentiment Analysis+2Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on the generality of the network given lim…
Anomaly DetectionData CompressionToward Efficient and Accurate Covariance Matrix Estimation on Compressed Data
Estimating covariance matrices is a fundamental technique in various domains, most notably in machine learning and signal processing. To tackle the challenges of extensive communication costs, large storage capacity…
Data CompressionEstimating the Fundamental Limits is Easier than Achieving the Fundamental Limits
We show through case studies that it is easier to estimate the fundamental limits of data processing than to construct explicit algorithms to achieve those limits. Focusing on binary classification, data compression, and…
Binary ClassificationData CompressionGeneral ClassificationThe Impact of Typicality for Informative Representative Selection
In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem. Furthermore, it is also useful to reduce the annot…
Active LearningData CompressionEfficient Manifold and Subspace Approximations with Spherelets
In statistical dimensionality reduction, it is common to rely on the assumption that high dimensional data tend to concentrate near a lower dimensional manifold. There is a rich literature on approximating the unknown ma…
ClusteringData CompressionDenoisingDimensionality ReductionFrom Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing
In recent studies [1][13][12] Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, data compression is a…
Data CompressionMultimodal deep learning approach for joint EEG-EMG data compression and classification
In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep autoencoder architecture which is desig…
Data CompressionDecoderEEGElectroencephalogram (EEG)+2A description length approach to determining the number of k-means clusters
We present an asymptotic criterion to determine the optimal number of clusters in k-means. We consider k-means as data compression, and propose to adopt the number of clusters that minimizes the estimated description len…
ClusteringData CompressionDimensionality ReductionTuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
Data compression is a popular technique for improving the efficiency of data processing workloads such as SQL queries and more recently, machine learning (ML) with classical batch gradient methods. But the efficacy of su…
Data CompressionOpen-Ended Question AnsweringText CompressionMaximum entropy models capture melodic styles
We introduce a Maximum Entropy model able to capture the statistics of melodies in music. The model can be used to generate new melodies that emulate the style of the musical corpus which was used to train it. Instead of…
Data CompressionMusic GenerationGlobally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary, based on which various successful applications have been achieved. Howe…
Data CompressionDictionary LearningJoint Data Compression and MAC Protocol Design for Smartgrids with Renewable Energy
In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which co…
compressed sensingData CompressionRandom Projection Estimation of Discrete-Choice Models with Large Choice Sets
We introduce sparse random projection, an important dimension-reduction tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are com…
Data CompressionDimensionality ReductionDiscrete Choice ModelsLEMMAXGBoost: A Scalable Tree Boosting System
Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-…
BIG-bench Machine LearningClusteringData CompressionDimensionality Reduction+3