66 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
XGBoost: A Scalable Tree Boosting System
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
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
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
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
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
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
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
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
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
Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use.