{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/data-compression/papers/4","list_of":"/task/data-compression","task":"Data Compression","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":5,"rows_per_page":100,"rows":[301,400],"of":459,"counts":{"archive_papers_tagged":459,"with_a_code_link":131,"where_syntology_ran_a_sample":33,"not_listed_spam_title":0,"listed":459,"listed_where_code_ran":33,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":32,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":32,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/data-compression","prev":"/task/data-compression/papers/3","next":"/task/data-compression/papers/5","papers":[{"url":null,"slug":"bayesian-nonparametric-estimation-of-coverage","title":"Bayesian nonparametric estimation of coverage probabilities and distinct counts from sketched data","date":"2022-09-05","arxiv_id":"2209.02135","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-low-complexity-approach-to-rate-distortion","title":"A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing","date":"2022-08-24","arxiv_id":"2208.11596","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-dynamic-mode-decomposition-workflow","title":"Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression","date":"2022-08-16","arxiv_id":"2208.07767","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-image-preprocessing-framework-for","title":"A Unified Image Preprocessing Framework For Image Compression","date":"2022-08-15","arxiv_id":"2208.07110","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-data-center-theories-and-applications","title":"Data centers with quantum random access memory and quantum networks","date":"2022-07-28","arxiv_id":"2207.14336","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-performance-of-data-compression-in","title":"On the Performance of Data Compression in Clustered Fog Radio Access Networks","date":"2022-07-01","arxiv_id":"2207.00223","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-algorithm-for-exact-concave-hull","title":"A Novel Algorithm for Exact Concave Hull Extraction","date":"2022-06-23","arxiv_id":"2206.11481","repositories_listed":0,"syntology":null},{"url":null,"slug":"riddle-lidar-data-compression-with-range-1","title":"RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding","date":"2022-06-02","arxiv_id":"2206.01738","repositories_listed":0,"syntology":null},{"url":null,"slug":"aerosense-a-self-sustainable-and-long-range","title":"Aerosense: A Self-Sustainable And Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines","date":"2022-05-24","arxiv_id":"2205.11902","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-shape-search-for-optimum-data","title":"Tensor Shape Search for Optimum Data Compression","date":"2022-05-21","arxiv_id":"2205.10651","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-architecture-and-high-throughput","title":"An Efficient Architecture and High-Throughput Implementation of CCSDS-123.0-B-2 Hybrid Entropy Coder Targeting Space-Grade SRAM FPGA Technology","date":"2022-05-09","arxiv_id":"2205.04123","repositories_listed":0,"syntology":null},{"url":null,"slug":"4dac-learning-attribute-compression-for","title":"4DAC: Learning Attribute Compression for Dynamic Point Clouds","date":"2022-04-25","arxiv_id":"2204.11723","repositories_listed":0,"syntology":null},{"url":null,"slug":"fpga-based-ai-smart-nics-for-scalable","title":"FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems","date":"2022-04-22","arxiv_id":"2204.10943","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-classification-at-the","title":"Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees","date":"2022-04-21","arxiv_id":"2204.10399","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-similarity-priors-neural-collages-as","title":"Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations","date":"2022-04-15","arxiv_id":"2204.07673","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-ideal-data-compression-and-automatic","title":"A unified theory of learning","date":"2022-03-31","arxiv_id":"2203.16941","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learning-framework-for-bandwidth-efficient","title":"A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT","date":"2022-03-17","arxiv_id":"2203.09631","repositories_listed":0,"syntology":null},{"url":null,"slug":"millimeter-scale-ultra-low-power-imaging","title":"Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring","date":"2022-03-09","arxiv_id":"2203.04496","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-validation-of-reinforcement-learning","title":"Reliable validation of Reinforcement Learning Benchmarks","date":"2022-03-02","arxiv_id":"2203.01075","repositories_listed":0,"syntology":null},{"url":null,"slug":"lcp-dropout-compression-based-multiple","title":"LCP-dropout: Compression-based Multiple Subword Segmentation for Neural Machine Translation","date":"2022-02-28","arxiv_id":"2202.13590","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognitive-semantic-communication-systems","title":"Cognitive Semantic Communication Systems Driven by Knowledge Graph","date":"2022-02-24","arxiv_id":"2202.11958","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-effective-and-robust-neural-trojan","title":"Towards Effective and Robust Neural Trojan Defenses via Input Filtering","date":"2022-02-24","arxiv_id":"2202.12154","repositories_listed":0,"syntology":null},{"url":null,"slug":"universality-of-parametric-coupling-flows","title":"Universality of parametric Coupling Flows over parametric diffeomorphisms","date":"2022-02-07","arxiv_id":"2202.02906","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-optimization-of-the-latency","title":"Analysis and Optimization of the Latency Budget in Wireless Systems with Mobile Edge Computing","date":"2022-01-27","arxiv_id":"2201.11590","repositories_listed":0,"syntology":null},{"url":null,"slug":"probability-distribution-on-rooted-trees","title":"Probability Distribution on Rooted Trees","date":"2022-01-24","arxiv_id":"2201.09460","repositories_listed":0,"syntology":null},{"url":null,"slug":"apack-off-chip-lossless-data-compression-for","title":"APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference","date":"2022-01-21","arxiv_id":"2201.08830","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-multi-modality-image-and-video","title":"Learning Based Multi-Modality Image and Video Compression","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"strategies-in-jpeg-compression-using","title":"Strategies in JPEG compression using Convolutional Neural Network (CNN)","date":"2021-12-06","arxiv_id":"2112.04500","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-sequential-coreset-method-for","title":"A Novel Sequential Coreset Method for Gradient Descent Algorithms","date":"2021-12-05","arxiv_id":"2112.02504","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-technique-for-compression-of-data-sets","title":"A new technique for compression of data sets","date":"2021-11-12","arxiv_id":"2111.06572","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-wedgelets-adaptive-data-compression-on","title":"Graph Wedgelets: Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets","date":"2021-10-17","arxiv_id":"2110.08843","repositories_listed":0,"syntology":null},{"url":null,"slug":"operationalizing-convolutional-neural-network","title":"Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery","date":"2021-10-10","arxiv_id":"2110.04906","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-twinning","title":"Data Twinning","date":"2021-10-06","arxiv_id":"2110.02927","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-gpu-implementation-of-randomized","title":"Efficient GPU implementation of randomized SVD and its applications","date":"2021-10-05","arxiv_id":"2110.03423","repositories_listed":0,"syntology":null},{"url":null,"slug":"probability-distribution-on-full-rooted-trees","title":"Probability Distribution on Full Rooted Trees","date":"2021-09-27","arxiv_id":"2109.12825","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-color-temperature-based-high-speed","title":"A color temperature-based high-speed decolorization: an empirical approach for tone mapping applications","date":"2021-08-31","arxiv_id":"2108.13656","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-dimensional-graph-fractional-fourier","title":"Multi-dimensional graph fractional Fourier transform and its application","date":"2021-08-28","arxiv_id":"2109.04358","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-image-coding-for-machines-a-content","title":"Learned Image Coding for Machines: A Content-Adaptive Approach","date":"2021-08-23","arxiv_id":"2108.09992","repositories_listed":0,"syntology":null},{"url":null,"slug":"samplets-a-new-paradigm-for-data-compression","title":"Samplets: A new paradigm for data compression","date":"2021-07-07","arxiv_id":"2107.03337","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascade-decoders-based-autoencoders-for-image","title":"Cascade Decoders-Based Autoencoders for Image Reconstruction","date":"2021-06-29","arxiv_id":"2107.00002","repositories_listed":0,"syntology":null},{"url":null,"slug":"inadvert-an-interactive-and-adaptive","title":"ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention","date":"2021-06-13","arxiv_id":"2106.06907","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-amplification-via-bernoulli-sampling","title":"Privacy Amplification Via Bernoulli Sampling","date":"2021-05-21","arxiv_id":"2105.10594","repositories_listed":0,"syntology":null},{"url":null,"slug":"tencent-video-dataset-tvd-a-video-dataset-for","title":"Tencent Video Dataset (TVD): A Video Dataset for Learning-based Visual Data Compression and Analysis","date":"2021-05-12","arxiv_id":"2105.05961","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-reconfigurable-neural-network-asic-for","title":"A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC","date":"2021-05-04","arxiv_id":"2105.01683","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-subset-selection-and-variable","title":"Bayesian subset selection and variable importance for interpretable prediction and classification","date":"2021-04-20","arxiv_id":"2104.10150","repositories_listed":0,"syntology":null},{"url":null,"slug":"turning-channel-noise-into-an-accelerator-for","title":"Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis","date":"2021-04-20","arxiv_id":"2104.10095","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-decade-of-research-for-image-compression-in","title":"A Decade of Research for Image Compression In Multimedia Laboratory","date":"2021-04-06","arxiv_id":"2105.09281","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-attention-networks-for-data","title":"Bayesian Attention Networks for Data Compression","date":"2021-03-29","arxiv_id":"2103.15319","repositories_listed":0,"syntology":null},{"url":null,"slug":"full-encoder-make-autoencoders-learn-like-pca","title":"Learning Stable Representations with Full Encoder","date":"2021-03-25","arxiv_id":"2103.14082","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-discovery-using-lossless-compression","title":"Data Discovery Using Lossless Compression-Based Sparse Representation","date":"2021-03-15","arxiv_id":"2103.08765","repositories_listed":0,"syntology":null},{"url":null,"slug":"grid-graph-signal-processing-grid-gsp-a-graph","title":"Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid","date":"2021-03-10","arxiv_id":"2103.06068","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-compression-with-bayesian-attention","title":"Data Compression with Bayesian Attention Networks","date":"2021-03-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-data-compression-for-physics-plasma","title":"Neural data compression for physics plasma simulation","date":"2021-03-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efloat-entropy-coded-floating-point-format","title":"EFloat: Entropy-coded Floating Point Format for Compressing Vector Embedding Models","date":"2021-02-04","arxiv_id":"2102.02705","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-intrusive-reduced-order-modeling-of","title":"Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation","date":"2021-01-28","arxiv_id":"2101.11810","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-missing-data-imputation-method-for-3d","title":"Anytime 3D Object Reconstruction using Multi-modal Variational Autoencoder","date":"2021-01-25","arxiv_id":"2101.10391","repositories_listed":0,"syntology":null},{"url":null,"slug":"overfitting-for-fun-and-profit-instance-1","title":"Overfitting for Fun and Profit: Instance-Adaptive Data Compression","date":"2021-01-21","arxiv_id":"2101.08687","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-black-box-2-0-efficient-high-bandwidth","title":"Smart Black Box 2.0: Efficient High-bandwidth Driving Data Collection based on Video Anomalies","date":"2021-01-03","arxiv_id":"2101.00706","repositories_listed":0,"syntology":null},{"url":null,"slug":"hard-masking-for-explaining-graph-neural","title":"Hard Masking for Explaining Graph Neural Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-grouping-and-denoising-via","title":"Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering","date":"2020-12-08","arxiv_id":"2012.04762","repositories_listed":0,"syntology":null},{"url":null,"slug":"lookahead-optimizer-improves-the-performance","title":"Lookahead optimizer improves the performance of Convolutional Autoencoders for reconstruction of natural images","date":"2020-12-03","arxiv_id":"2012.05694","repositories_listed":0,"syntology":null},{"url":null,"slug":"hm-ann-efficient-billion-point-nearest","title":"HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-approach-of-data-pre-processing-for","title":"A New Approach of Data Pre-processing for Data Compression in Smart Grids","date":"2020-11-24","arxiv_id":"2011.12361","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximum-likelihood-estimation-in-data-driven","title":"Maximum Likelihood Estimation in Data-Driven Modeling and Control","date":"2020-11-02","arxiv_id":"2011.00925","repositories_listed":0,"syntology":null},{"url":null,"slug":"distance-invariant-sparse-autoencoder-for","title":"Distance Invariant Sparse Autoencoder for Wireless Signal Strength Mapping","date":"2020-10-29","arxiv_id":"2010.15347","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-compression-of-mri-data-modular","title":"Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding","date":"2020-10-08","arxiv_id":"2010.04065","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-and-sparse-enhanced-tucker","title":"Low-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion","date":"2020-10-01","arxiv_id":"2010.00359","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-the-effect-of-image-compression","title":"Quantifying the effect of image compression on supervised learning applications in optical microscopy","date":"2020-09-26","arxiv_id":"2009.12570","repositories_listed":0,"syntology":null},{"url":null,"slug":"analog-vs-digital-spatial-transforms-a","title":"Analog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison","date":"2020-09-15","arxiv_id":"2009.07332","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-energy-efficient-wireless-neural-recording","title":"An Energy-efficient Wireless Neural Recording System with Compressed Sensing and Encryption","date":"2020-09-14","arxiv_id":"2009.06532","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-data-compression-for-approximate","title":"Deep data compression for approximate ultrasonic image formation","date":"2020-09-04","arxiv_id":"2009.02293","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-subband-compression-for-streaming-of","title":"Adaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data","date":"2020-08-23","arxiv_id":"2008.10092","repositories_listed":0,"syntology":null},{"url":null,"slug":"exposing-deep-faked-videos-by-anomalous-co","title":"Exposing Deep-faked Videos by Anomalous Co-motion Pattern Detection","date":"2020-08-11","arxiv_id":"2008.04848","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-in-the-creative","title":"Artificial Intelligence in the Creative Industries: A Review","date":"2020-07-24","arxiv_id":"2007.12391","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quick-review-on-recent-trends-in-3d-point","title":"A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain","date":"2020-07-08","arxiv_id":"2007.05038","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-dual-comb-spectroscopy","title":"Compressive dual-comb spectroscopy","date":"2020-07-03","arxiv_id":"2007.03761","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multiparametric-class-of-low-complexity","title":"A Multiparametric Class of Low-complexity Transforms for Image and Video Coding","date":"2020-06-19","arxiv_id":"2006.11418","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-domain-detection-and-estimation","title":"Compressed-Domain Detection and Estimation for Colocated MIMO Radar","date":"2020-06-17","arxiv_id":"2006.09860","repositories_listed":0,"syntology":null},{"url":null,"slug":"physically-interpretable-machine-learning","title":"Physically interpretable machine learning algorithm on multidimensional non-linear fields","date":"2020-05-28","arxiv_id":"2005.13912","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-based-information-compression-for-multi","title":"Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels","date":"2020-05-28","arxiv_id":"2005.14220","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-compression-ratio-selection-for-edge","title":"Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines","date":"2020-05-25","arxiv_id":"2005.12235","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-compression-rate-of-quantum-autoencoders","title":"On compression rate of quantum autoencoders: Control design, numerical and experimental realization","date":"2020-05-22","arxiv_id":"2005.11149","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimal-transport-approach-to-data","title":"The optimal transport paradigm enables data compression in data-driven robust control","date":"2020-05-19","arxiv_id":"2005.09393","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-privacy-preserving-edge-computing","title":"Efficient Privacy Preserving Edge Computing Framework for Image Classification","date":"2020-05-10","arxiv_id":"2005.04563","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-l21-based-semi-nonnegative-matrix","title":"Regularized L21-Based Semi-NonNegative Matrix Factorization","date":"2020-05-10","arxiv_id":"2005.04602","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-training-of-physics-informed-neural","title":"Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations","date":"2020-05-02","arxiv_id":"2005.05092","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-computing-for-smart-health-context-aware","title":"Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges","date":"2020-04-15","arxiv_id":"2004.07311","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-generalized-rate-distortion","title":"Characterizing Generalized Rate-Distortion Performance of Video Coding: An Eigen Analysis Approach","date":"2020-04-13","arxiv_id":"1912.07126","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedback-recurrent-autoencoder-for-video","title":"Feedback Recurrent Autoencoder for Video Compression","date":"2020-04-09","arxiv_id":"2004.04342","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovering-compressed-images-for-automatic","title":"Recovering compressed images for automatic crack segmentation using generative models","date":"2020-03-06","arxiv_id":"2003.03028","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grain-atlases-of-functional-modes-for","title":"Fine-grain atlases of functional modes for fMRI analysis","date":"2020-03-05","arxiv_id":"2003.05405","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-auto-encoder-based-deep-reinforcement","title":"Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks","date":"2020-01-24","arxiv_id":"2001.09223","repositories_listed":0,"syntology":null},{"url":null,"slug":"variable-bitrate-neural-compression-via-1","title":"Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-approximation-of-rough-functions-with","title":"On the approximation of rough functions with deep neural networks","date":"2019-12-13","arxiv_id":"1912.06732","repositories_listed":0,"syntology":null},{"url":null,"slug":"matrix-sketching-for-supervised","title":"Matrix sketching for supervised classification with imbalanced classes","date":"2019-12-02","arxiv_id":"1912.00905","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-projections-with-asymmetric","title":"Random Projections with Asymmetric Quantization","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"flatsomatic-a-method-for-compression-of","title":"Flatsomatic: A Method for Compression of Somatic Mutation Profiles in Cancer","date":"2019-11-27","arxiv_id":"1911.13259","repositories_listed":0,"syntology":null},{"url":null,"slug":"enumerative-data-compression-with-non","title":"Enumerative Data Compression with Non-Uniquely Decodable Codes","date":"2019-11-13","arxiv_id":"1911.05676","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-underwater-acoustic-communications","title":"Compressed Underwater Acoustic Communications for Dynamic Interaction with Underwater Vehicles","date":"2019-11-11","arxiv_id":"1911.04072","repositories_listed":0,"syntology":null},{"url":null,"slug":"parametric-context-adaptive-laplace","title":"Parametric context adaptive Laplace distribution for multimedia compression","date":"2019-10-14","arxiv_id":"1906.03238","repositories_listed":0,"syntology":null}],"record_sha256":"2c18897dc37789a7b06586f01679fb75c829b0a50980fba7c7bcc56f9181c90f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}