Data Compression

66 papers with code • 0 benchmarks • 0 datasets

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Use these libraries to find Data Compression models and implementations
2 papers

Most implemented papers

XGBoost: A Scalable Tree Boosting System

dmlc/xgboost 9 Mar 2016

In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.

Efficient Manifold and Subspace Approximations with Spherelets

david-dunson/GeodesicDistance 26 Jun 2017

There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction.

Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search

xinyandai/product-quantization 12 Nov 2019

In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error.

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

Ma-Lab-Berkeley/ReduNet 21 May 2021

This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminative representation.

Supervised Compression for Resource-Constrained Edge Computing Systems

yoshitomo-matsubara/supervised-compression 21 Aug 2021

There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.

BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing

yoshitomo-matsubara/bottlefit-split_computing 7 Jan 2022

We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w. r. t.)

DC-BENCH: Dataset Condensation Benchmark

justincui03/dc_benchmark 20 Jul 2022

Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset.

SegMap: 3D Segment Mapping using Data-Driven Descriptors

ethz-asl/segmap 25 Apr 2018

While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.

XGBoost: Scalable GPU Accelerated Learning

dmlc/xgboost 29 Jun 2018

We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github. com/dmlc/xgboost).

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features

mistic-lab/IPSW-RFI 22 Jul 2018

Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use.