Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Integer Networks for Data Compression with Latent-Variable Models
We consider the problem of using variational latent-variable models for data compression. For such models to produce a compressed binary sequence, which is the universal data representation in a digital world, the latent…
Data CompressionMathSemisupervised Clustering by 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 po…
ClusteringData CompressionPhysical-Layer Supervised Learning Assisted by an Entangled Sensor Network
Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedu…
Data CompressionDeepZip: Lossless Data Compression using Recurrent Neural Networks
Sequential data is being generated at an unprecedented pace in various forms, including text and genomic data. This creates the need for efficient compression mechanisms to enable better storage, transmission and process…
Data CompressionGaussian AutoEncoder
Generative AutoEncoders require a chosen probability distribution in latent space, usually multivariate Gaussian. The original Variational AutoEncoder (VAE) uses randomness in encoder - causing problematic distortion, an…
Data CompressionQuantizationA Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments
In the era of big data, practical applications in various domains continually generate large-scale time-series data. Among them, some data show significant or potential periodicity characteristics, such as meteorological…
Cloud ComputingData CompressionDistributed ComputingPrediction+3Deep Surface Light Fields
A surface light field represents the radiance of rays originating from any points on the surface in any directions. Traditional approaches require ultra-dense sampling to ensure the rendering quality. In this paper, we p…
Data CompressionGPUImage RegistrationA Novel Chaotic Uniform Quantizer for Speech Coding
Quantization is an essential step in the analog-to-digital conversion process and it is very important in all modern telecommunication systems. In this paper, a novel chaotic uniform quantizer is proposed and its applica…
Data CompressionQuantizationRate Distortion For Model Compression: From Theory To Practice
The enormous size of modern deep neural networks makes it challenging to deploy those models in memory and communication limited scenarios. Thus, compressing a trained model without a significant loss in performance has …
Data CompressionModel CompressionQuantizationSemantic Topic Analysis of Traffic Camera Images
Traffic cameras are commonly deployed monitoring components in road infrastructure networks, providing operators visual information about conditions at critical points in the network. However, human observers are often l…
Data CompressionObject RecognitionPerson Identification using Seismic Signals generated from Footfalls
Footfall based biometric system is perhaps the only person identification technique which does not hinder the natural movement of an individual. This is a clear edge over all other biometric systems which require a formi…
Data CompressionPerson IdentificationOn the Information in Extreme Measurements for Parameter Estimation
This paper deals with parameter estimation from extreme measurements. While being a special case of parameter estimation from partial data, in scenarios where only one sample from a given set of K measurements can be ext…
Data Compressionparameter estimationIntermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing
The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead…
Data CompressionDeep LearningFeature CompressionAn iterative method for classification of binary data
In today's data driven world, storing, processing, and gleaning insights from large-scale data are major challenges. Data compression is often required in order to store large amounts of high-dimensional data, and thus, …
ClassificationData CompressionGeneral ClassificationMatrix Factorization on GPUs with Memory Optimization and Approximate Computing
Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. While many proble…
Collaborative FilteringCPUData CompressionGPUSAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features
Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an un…
Anomaly DetectionData CompressionFast Fourier-Based Generation of the Compression Matrix for Deterministic Compressed Sensing
The primary goal of this work is to review the importance of data compression and present a fast Fourier-based method for generating the deterministic compression matrix in the area of deterministic compressed sensing. T…
compressed sensingData CompressionXGBoost: Scalable GPU Accelerated Learning
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GPU systems with all of the features of th…
Cloud ComputingData CompressionGPUHuman Activity Prediction in Smart Home Environments with LSTM Neural Networks
In this paper, we investigate the performance of several sequence prediction techniques on the prediction of future events of human behavior in a smart home, as well as the timestamps of those next events. Prediction tec…
Activity PredictionActivity RecognitionData CompressionHome Activity Monitoring+3SegMap: 3D Segment Mapping using Data-Driven Descriptors
When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination. This paper presents SegMap: a map represent…
Data Compression