Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocatio…
Data CompressionDeep Reinforcement LearningEdge-computingReinforcement Learning+1Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding
Deep Bayesian latent variable models have enabled new approaches to both model and data compression. Here, we propose a new algorithm for compressing latent representations in deep probabilistic models, such as variation…
Data CompressionImage CompressionWord EmbeddingsOn the approximation of rough functions with deep neural networks
Deep neural networks and the ENO procedure are both efficient frameworks for approximating rough functions. We prove that at any order, the ENO interpolation procedure can be cast as a deep ReLU neural network. This surp…
Data CompressionCORAD: Correlation-Aware Compression of Massive Time Series using Sparse Dictionary Coding
Time series streams are ubiquitous in many application domains, e.g., transportation, network monitoring, autonomous vehicles, or the Internet of Things (IoT). Transmitting and storing large amounts of such fine-grained …
Autonomous VehiclesData CompressionDictionary LearningLossy-Compression Artifact Reduction+4Matrix sketching for supervised classification with imbalanced classes
Matrix sketching is a recently developed data compression technique. An input matrix A is efficiently approximated with a smaller matrix B, so that B preserves most of the properties of A up to some guaranteed approximat…
ClassificationData CompressionGeneral ClassificationRandom Projections with Asymmetric Quantization
The method of random projection has been a popular tool for data compression, similarity search, and machine learning. In many practical scenarios, applying quantization on randomly projected data could be very helpful t…
Data CompressionQuantizationFlatsomatic: A Method for Compression of Somatic Mutation Profiles in Cancer
In this study, we present Flatsomatic - a Variational Auto Encoder (VAE) optimized to compress somatic mutations that allow for unbiased data compression whilst maintaining the signal. We compared two different neural ne…
ClusteringData CompressionEnumerative Data Compression with Non-Uniquely Decodable Codes
Non-uniquely decodable codes can be defined as the codes that cannot be uniquely decoded without additional disambiguation information. These are mainly the class of non-prefix-free codes, where a codeword can be a prefi…
Data CompressionNorm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
Vector quantization (VQ) techniques are widely used in similarity search for data compression, fast metric computation and etc. Originally designed for Euclidean distance, existing VQ techniques (e.g., PQ, AQ) explicitly…
Data CompressionQuantizationCompressed Underwater Acoustic Communications for Dynamic Interaction with Underwater Vehicles
Underwater vehicles are utilized in various applications including underwater data-collection missions. The tethered connection constrains the mission both in distance traveled and number of vehicles that can run in the …
Data CompressionLFZip: Lossy compression of multivariate floating-point time series data via improved prediction
Time series data compression is emerging as an important problem with the growth in IoT devices and sensors. Due to the presence of noise in these datasets, lossy compression can often provide significant compression gai…
Data CompressionQuantizationTime SeriesTime Series AnalysisParametric context adaptive Laplace distribution for multimedia compression
Data compression often subtracts prediction and encodes the difference (residue) e.g. assuming Laplace distribution, for example for images, videos, audio, or numerical data. Its performance is strongly dependent on the …
Data CompressionQuantizationLossless Data Compression with Transformer
Transformers have replaced long-short term memory and other recurrent neural networks variants in sequence modeling. It achieves state-of-the-art performance on a wide range of tasks related to natural language processin…
Data CompressionLanguage ModelingLanguage ModellingMachine Translation+2QuicK-means: Acceleration of K-means by learning a fast transform
K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be. It has indeed proven pivotal to help increase the speed of many machine learning and data analysis tec…
ClusteringData CompressionPareto-optimal data compression for binary classification tasks
The goal of lossy data compression is to reduce the storage cost of a data set $X$ while retaining as much information as possible about something ($Y$) that you care about. For example, what aspects of an image $X$ cont…
Binary ClassificationClassificationClusteringData Compression+3An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning
Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates tra…
Data CompressionFederated LearningMinimum Description Length Revisited
This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern re…
Data CompressionModel SelectionTwo-sample testingAn Interpretable Compression and Classification System: Theory and Applications
This study proposes a low-complexity interpretable classification system. The proposed system contains three main modules including feature extraction, feature reduction, and classification. All of them are linear. Thank…
ClassificationData CompressionGeneral ClassificationThe PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization
Mining and exploring databases should provide users with knowledge and new insights. Tiles of data strive to unveil true underlying structure and distinguish valuable information from various kinds of noise. We propose a…
Data CompressionModel SelectionAdaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression
We propose Adaptive Sample-space & Adaptive Probability (ASAP) coding, an efficient neural-network based method for lossy data compression. Our ASAP coding distinguishes itself from the conventional method based on adapt…
Data CompressionEfficient Neural NetworkMS-SSIMQuantization+1