Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?
In this paper, we contend that a natural objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a low-dimensional Gaussian mixture supported on incoher…
AllData CompressionDenoisingRepresentation LearningLearning from similar systems and online data-driven LQR using iterative randomised data compression
The problem of data-driven recursive computation of receding horizon LQR control through a randomized combination of online/current and historical/recorded data is considered. It is assumed that large amounts of historic…
Data CompressionConvolve and Conquer: Data Comparison with Wiener Filters
Quantitative evaluations of differences and/or similarities between data samples define and shape optimisation problems associated with learning data distributions. Current methods to compare data often suffer from limit…
Data CompressionImputationAccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers h…
Data CompressionBlurry Video Compression: A Trade-off between Visual Enhancement and Data Compression
Existing video compression (VC) methods primarily aim to reduce the spatial and temporal redundancies between consecutive frames in a video while preserving its quality. In this regard, previous works have achieved remar…
Data CompressionVideo CompressionHopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain State Decoding
The study of brain states, ranging from highly synchronous to asynchronous neuronal patterns like the sleep-wake cycle, is fundamental for assessing the brain's spatiotemporal dynamics and their close connection to behav…
BenchmarkingData CompressionFast Machine Learning Method with Vector Embedding on Orthonormal Basis and Spectral Transform
This paper presents a novel fast machine learning method that leverages two techniques: Vector Embedding on Orthonormal Basis (VEOB) and Spectral Transform (ST). The VEOB converts the original data encoding into a vector…
Data CompressionFast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data
High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high-energy physics. Developing real-time data compression algor…
Data CompressionSimBIG: Field-level Simulation-Based Inference of Galaxy Clustering
We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing summary statistics, such as the power…
ClusteringData CompressionMirage: Model-Agnostic Graph Distillation for Graph Classification
GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments. Graph distillation is an ef…
BenchmarkingClassificationData CompressionGraph Classification+1A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression
The lack of an efficient compression model remains a challenge for the wireless transmission of gearbox data in non-contact gear fault diagnosis problems. In this paper, we present a signal-adaptive asymmetrical autoenco…
Data CompressionFault DiagnosisECNR: Efficient Compressive Neural Representation of Time-Varying Volumetric Datasets
Due to its conceptual simplicity and generality, compressive neural representation has emerged as a promising alternative to traditional compression methods for managing massive volumetric datasets. The current practice …
Data CompressionRECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations
COMpression with Bayesian Implicit NEural Representations (COMBINER) is a recent data compression method that addresses a key inefficiency of previous Implicit Neural Representation (INR)-based approaches: it avoids quan…
Data CompressionQuantizationContext-Aware Neural Video Compression on Solar Dynamics Observatory
NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminati…
Data CompressionVideo CompressionDoes Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization
Video summarization remains a huge challenge in computer vision due to the size of the input videos to be summarized. We propose an efficient, language-only video summarizer that achieves competitive accuracy with high d…
Data CompressionVideo SummarizationElectroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer
Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete co…
Data CompressionDecoderEEGElectroencephalogram (EEG)Bandwidth-efficient Inference for Neural Image Compression
With neural networks growing deeper and feature maps growing larger, limited communication bandwidth with external memory (or DRAM) and power constraints become a bottleneck in implementing network inference on mobile an…
Data CompressionImage CompressionQuantizationSymED: Adaptive and Online Symbolic Representation of Data on the Edge
The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-c…
Anomaly DetectionData CompressionDynamic Time WarpingEdge-computingStreaming Compression of Scientific Data via weak-SINDy
In this paper a streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. The production of scientific data, either via simulation or experiments, is undergoing an stage of expon…
Data CompressionCompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical Images
Medical image compression is a widely studied field of data processing due to its prevalence in modern digital databases. This domain requires a high color depth of 12 bits per pixel component for accurate analysis by ph…
Data CompressionImage Compression