Dimensionality Reduction
381 papers with code • 0 benchmarks • 11 datasets
Dimensionality reduction is the task of reducing the dimensionality of a dataset.
( Image credit: openTSNE )
Benchmarks
Datasets
Oxford105k
Isolet
Oxford-Affine
GoodSounds
SoF
AtariARI
Deep Fakes Dataset
Greatest papers with code
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
Unifying Deep Local and Global Features for Image Search
tensorflow/models
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ECCV 2020
Image retrieval is the problem of searching an image database for items that are similar to a query image.
Automatic Differentiation in PyTorch
pytorch/pytorch
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NIPS 2017 2017
In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Caffe: Convolutional Architecture for Fast Feature Embedding
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
google-research/google-research
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NeurIPS 2019
We give a new analysis of this sketch, providing nearly optimal bounds.
CNTK: Microsoft's Open-Source Deep-Learning Toolkit
Microsoft/CNTK
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ACM SIGKDD 2016
This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep-learning toolkit for Windows and Linux.
Rethinking Spatial Dimensions of Vision Transformers
rwightman/pytorch-image-models
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We empirically show that such a spatial dimension reduction is beneficial to a transformer architecture as well, and propose a novel Pooling-based Vision Transformer (PiT) upon the original ViT model.
Ranked #105 on
Image Classification
on ImageNet
