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Data Compression

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Showing 301–325 of 459 papers

TitleStatusHype
Towards efficient compression and communication for prototype-based decentralized learning—0
Enabling DBSCAN for Very Large-Scale High-Dimensional Spaces—0
Towards Neuromorphic Compression based Neural Sensing for Next-Generation Wireless Implantable Brain Machine Interface—0
Towards Optimal Learning of Language Models—0
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression—0
Training Foundation Models as Data Compression: On Information, Model Weights and Copyright Law—0
TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation—0
Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunities—0
Transformers learn variable-order Markov chains in-context—0
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent—0
"Turing Tests" For An AI Scientist—0
Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis—0
Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations—0
Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets—0
Universality of parametric Coupling Flows over parametric diffeomorphisms—0
Universal Memcomputing Machines—0
Universal Weak Coreset—0
Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison—0
VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification—0
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding—0
Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps in Reacting PIV Data—0
Lossless Data Compression with Transformer—0
Low-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion—0
Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces—0
Mathematical model of parameters relevance in adaptive level-crossing sampling for electrocardiogram signals—0
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