Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
A Decade of Research for Image Compression In Multimedia Laboratory
With the advancement of technology, we have supercomputers with high processing power and affordable prices. In addition, using multimedia expanded all around the world. This caused a vast use of images and videos in dif…
Data CompressionImage CompressionBayesian Attention Networks for Data Compression
The lossless data compression algorithm based on Bayesian Attention Networks is derived from first principles. Bayesian Attention Networks are defined by introducing an attention factor per a training sample loss as a fu…
Data CompressionPredictionLearning Stable Representations with Full Encoder
While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA (from the linear domain) does, we propose Full Encoder, a novel unified autoen…
Anomaly DetectionData CompressionData Discovery Using Lossless Compression-Based Sparse Representation
Sparse representation has been widely used in data compression, signal and image denoising, dimensionality reduction and computer vision. While overcomplete dictionaries are required for sparse representation of multidim…
Data CompressionDenoisingDimensionality ReductionImage DenoisingA semi-agnostic ansatz with variable structure for quantum machine learning
Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials…
BIG-bench Machine LearningData CompressionQuantum Machine LearningGrid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid
The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework. Grid-GSP provides an int…
Data CompressionGraph Autoencoder for Graph Compression and Representation Learning
We consider the problem of graph data compression and representation. Recent developments in graph neural networks (GNNs) focus on generalizing convolutional neural networks (CNNs) to graph data, which includes redesigni…
Data CompressionGraph ClassificationGraph Representation LearningRepresentation LearningNeural data compression for physics plasma simulation
We present a VAE-based data compression method, called VAe Physics Optimized Reduction (VAPOR), to compress scientific data while preserving physics constraints. VAPOR is based on Vector Quantized Variational Auto Encode…
Data CompressionData Compression with Bayesian Attention Networks
The lossless data compression algorithm based on Bayesian Attention Networks is derived from first principles.
Data CompressionCOIN: COmpression with Implicit Neural representations
We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image. Specifically, to encode an image, we…
Data CompressionImage CompressionImproving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal to the KL divergence between the approxi…
Data CompressionYou Only Compress Once: Optimal Data Compression for Estimating Linear Models
Linear models are used in online decision making, such as in machine learning, policy algorithms, and experimentation platforms. Many engineering systems that use linear models achieve computational efficiency through di…
Computational EfficiencyData CompressionDecision MakingAn Information-Theoretic Justification for Model Pruning
We study the neural network (NN) compression problem, viewing the tension between the compression ratio and NN performance through the lens of rate-distortion theory. We choose a distortion metric that reflects the effec…
Data CompressionmodelModel CompressionCLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback
Unleashing the full potential of massive MIMO in FDD mode by reducing the overhead of CSI feedback has recently garnered attention. Numerous deep learning for massive MIMO CSI feedback approaches have demonstrated their …
Compressive SensingData CompressionDeep LearningImage CompressionEFloat: Entropy-coded Floating Point Format for Compressing Vector Embedding Models
In a large class of deep learning models, including vector embedding models such as word and database embeddings, we observe that floating point exponent values cluster around a few unique values, permitting entropy base…
Data CompressionNon-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
We present a non-intrusive model reduction framework for linear poroelasticity problems in heterogeneous porous media using proper orthogonal decomposition (POD) and neural networks, based on the usual offline-online par…
Data CompressionAnytime 3D Object Reconstruction using Multi-modal Variational Autoencoder
For effective human-robot teaming, it is important for the robots to be able to share their visual perception with the human operators. In a harsh remote collaboration setting, data compression techniques such as autoenc…
3D Object Reconstruction3D Shape ReconstructionData CompressionImputation+3Overfitting for Fun and Profit: Instance-Adaptive Data Compression
Neural data compression has been shown to outperform classical methods in terms of $RD$ performance, with results still improving rapidly. At a high level, neural compression is based on an autoencoder that tries to reco…
Data CompressionImage CompressionQuantizationGeneralized Image Reconstruction over T-Algebra
Principal Component Analysis (PCA) is well known for its capability of dimension reduction and data compression. However, when using PCA for compressing/reconstructing images, images need to be recast to vectors. The vec…
Data CompressionDimensionality ReductionImage ReconstructionSmart Black Box 2.0: Efficient High-bandwidth Driving Data Collection based on Video Anomalies
Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The Smart Black Box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-ban…
Action DetectionAnomaly DetectionAutonomous VehiclesData Compression