Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Hard Masking for Explaining Graph Neural Networks
Graph Neural Networks (GNNs) are a flexible and powerful family of models that build nodes' representations on irregular graph-structured data. This paper focuses on explaining or interpreting the rationale underlying a …
Data CompressionDecision MakingNode ClassificationPredictionNeural Joint Entropy Estimation
Estimating the entropy of a discrete random variable is a fundamental problem in information theory and related fields. This problem has many applications in various domains, including machine learning, statistics and da…
Data CompressionMutual Information EstimationCommunication-Efficient Federated Learning with Compensated Overlap-FedAvg
Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the apprehension of data & privacy leakage, …
Data CompressionFederated LearningSimultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perhaps using wavelet denoising, and then to…
ClusteringData CompressionDenoisingLookahead optimizer improves the performance of Convolutional Autoencoders for reconstruction of natural images
Autoencoders are a class of artificial neural networks which have gained a lot of attention in the recent past. Using the encoder block of an autoencoder the input image can be compressed into a meaningful representation…
Data CompressionDecoderDenoisingHM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory
The state-of-the-art approximate nearest neighbor search (ANNS) algorithms face a fundamental tradeoff between query latency and accuracy, because of small main memory capacity: To store indices in main memory for short …
Data CompressionQuantizationA New Approach of Data Pre-processing for Data Compression in Smart Grids
The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Lo\`{e}ve, or wavelet transforms. One drawback from adopting such an approach is that it is independe…
Data CompressionDecision MakingMaximum Likelihood Estimation in Data-Driven Modeling and Control
Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these methods lead to ill-conditioned data-driven mo…
Data CompressionLEMMADistance Invariant Sparse Autoencoder for Wireless Signal Strength Mapping
Wireless signal strength based localization can enable robust localization for robots using inexpensive sensors. For this, a location-to-signal-strength map has to be learned for each access point in the environment. Due…
Data CompressionRegularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding
The Magnetic Resonance Imaging (MRI) processing chain starts with a critical acquisition stage that provides raw data for reconstruction of images for medical diagnosis. This flow usually includes a near-lossless data co…
Data CompressionImage CompressionMedical DiagnosisMRI ReconstructionSelf-Supervised Variational Auto-Encoders
Density estimation, compression and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of g…
Data CompressionDensity EstimationEdge DetectionLow-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion
Tensor completion refers to the task of estimating the missing data from an incomplete measurement or observation, which is a core problem frequently arising from the areas of big data analysis, computer vision, and netw…
Data CompressionFace RecognitionQuantifying the effect of image compression on supervised learning applications in optical microscopy
The impressive growth of data throughput in optical microscopy has triggered a widespread use of supervised learning (SL) models running on compressed image datasets for efficient automated analysis. However, since lossy…
Cell SegmentationData CompressionImage CompressionAnalog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison
Spatial linear transforms that process multiple parallel analog signals to simplify downstream signal processing find widespread use in multi-antenna communication systems, machine learning inference, data compression, a…
Data CompressionAn Energy-efficient Wireless Neural Recording System with Compressed Sensing and Encryption
This paper presents a wireless neural recording system featuring energy-efficient data compression and encryption. An ultra-high efficiency is achieved by leveraging compressed sensing (CS) for simultaneous data compress…
compressed sensingData CompressionDeep data compression for approximate ultrasonic image formation
In many ultrasonic imaging systems, data acquisition and image formation are performed on separate computing devices. Data transmission is becoming a bottleneck, thus, efficient data compression is essential. Compression…
Data CompressionDecoderQuantizationAdaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data
A data compression system capable of providing real-time streaming of high-resolution continuous point-on-wave (CPOW) and phasor measurement unit (PMU) measurements is proposed. Referred to as adaptive subband compressio…
Data CompressionExposing Deep-faked Videos by Anomalous Co-motion Pattern Detection
Recent deep learning based video synthesis approaches, in particular with applications that can forge identities such as "DeepFake", have raised great security concerns. Therefore, corresponding deep forensic methods are…
Anomaly DetectionData CompressionFace SwappingArtificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) al…
BIG-bench Machine LearningData CompressionDeep Reinforcement LearningA Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain
Automatic processing of 3D Point Cloud data for object detection, tracking and segmentation is the latest trending research in the field of AI and Data Science, which is specifically aimed at solving different challenges…
Autonomous DrivingData Compressionobject-detectionObject Detection