Data Compression

47 papers with code • 0 benchmarks • 0 datasets

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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.

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.)

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.

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

cuMF/cumf_als 11 Aug 2018

Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficient.

DeepZip: Lossless Data Compression using Recurrent Neural Networks

mohit1997/DeepZip 20 Nov 2018

We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets.