Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Rate-Constrained Quantization for Communication-Efficient Federated Learning
Quantization is a common approach to mitigate the communication cost of federated learning (FL). In practice, the quantized local parameters are further encoded via an entropy coding technique, such as Huffman coding, fo…
Data CompressionFederated LearningQuantizationAttention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential growth in data production is surpassing …
Data CompressionBWT construction and search at the terabase scale
Motivation: Burrows-Wheeler Transform (BWT) is a common component in full-text indices. Initially developed for data compression, it is particularly powerful for encoding redundant sequences such as pangenome data. Howev…
Data CompressionNDP: Next Distribution Prediction as a More Broad Target
Large language models (LLMs) trained on next-token prediction (NTP) paradigm have demonstrated powerful capabilities. However, the existing NTP paradigm contains several limitations, particularly related to planned task …
Data CompressionDomain AdaptationPredictionTranslationSelf-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays
The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to handle the vast amounts of data generated by these devices. Chemiresistive sensor arrays (CSAs), a simple-to-fabr…
Data CompressionDimensionality ReductionBandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning
Current data compression methods, such as sparsification in Federated Averaging (FedAvg), effectively enhance the communication efficiency of Federated Learning (FL). However, these methods encounter challenges such as t…
Data CompressionFederated LearningLLM-PCGC: Large Language Model-based Point Cloud Geometry Compression
The key to effective point cloud compression is to obtain a robust context model consistent with complex 3D data structures. Recently, the advancement of large language models (LLMs) has highlighted their capabilities no…
Data CompressionIn-Context LearningLanguage ModelingLanguage Modelling+1Semantic Variational Bayes Based on a Semantic Information Theory for Solving Latent Variables
The Variational Bayesian method (VB) is used to solve the probability distributions of latent variables with the minimum free energy criterion. This criterion is not easy to understand, and the computation is complex. Fo…
Data CompressionEdge Learning Based Collaborative Automatic Modulation Classification for Hierarchical Cognitive Radio Networks
In hierarchical cognitive radio networks, edge or cloud servers utilize the data collected by edge devices for modulation classification, which, however, is faced with problems of the computation load, transmission overh…
ClassificationData CompressionSemantic CompressionScaling Training Data with Lossy Image Compression
Empirically-determined scaling laws have been broadly successful in predicting the evolution of large machine learning models with training data and number of parameters. As a consequence, they have been useful for optim…
Data CompressionImage CompressionAccelerating the Low-Rank Decomposed Models
Tensor decomposition is a mathematically supported technique for data compression. It consists of applying some kind of a Low Rank Decomposition technique on the tensors or matrices in order to reduce the redundancy of t…
Data CompressionTensor DecompositionA Benchmark for Gaussian Splatting Compression and Quality Assessment Study
To fill the gap of traditional GS compression method, in this paper, we first propose a simple and effective GS data compression anchor called Graph-based GS Compression (GGSC). GGSC is inspired by graph signal processin…
AttributeData CompressionQuantizationTraining Foundation Models as Data Compression: On Information, Model Weights and Copyright Law
The training process of foundation models as for other classes of deep learning systems is based on minimizing the reconstruction error over a training set. For this reason, they are susceptible to the memorization and s…
Data CompressionMemorizationEnhancing Electrocardiogram Signal Analysis Using NLP-Inspired Techniques: A Novel Approach with Embedding and Self-Attention
A language is made up of an infinite/finite number of sentences, which in turn is composed of a number of words. The Electrocardiogram (ECG) is the most popular noninvasive medical tool for studying heart function and di…
Data CompressionNeural-based Video Compression on Solar Dynamics Observatory Images
NASA's Solar Dynamics Observatory (SDO) mission collects extensive data to monitor the Sun's daily activity. In the realm of space mission design, data compression plays a crucial role in addressing the challenges posed …
Data CompressionVideo CompressionCADC: Encoding User-Item Interactions for Compressing Recommendation Model Training Data
Deep learning recommendation models (DLRMs) are at the heart of the current e-commerce industry. However, the amount of training data used to train these large models is growing exponentially, leading to substantial trai…
Data CompressionUsing Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison
Low-discrepancy points (also called Quasi-Monte Carlo points) are deterministically and cleverly chosen point sets in the unit cube, which provide an approximation of the uniform distribution. We explore two methods base…
ClusteringData CompressionEntropy Law: The Story Behind Data Compression and LLM Performance
Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much less computational overhead. Most metho…
Data CompressionKeyVideoLLM: Towards Large-scale Video Keyframe Selection
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important. Video Large Language Models (VideoLLMs) have emerged in recent years due to their strong vid…
Data CompressionManagementQuestion AnsweringVideo Question Answering+1An AI-based, Error-bounded Compression Scheme for High-frequency Power Quality Disturbance Data
The implementation of modern monitoring systems for power quality disturbances have the potential to generate substantial amounts of data, reaching a point where transmission and storage of high-frequency measurements be…
Data Compression