Papers with Code 아카이브

Dimensionality Reduction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 2
평가표: 2
보존된 평가 기록: 6

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

<span style="color:grey; opacity: 0.6">( Image credit: openTSNE )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/dineshkr/fastembed

공식 코드

Compressive spectral embedding: sidestepping the SVD

https://cran.r-project.org/package=cml

공식 코드

Dimension Reduction with Prior Information for Knowledge Discovery

https://cran.r-project.org/package=msu

공식 코드

Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection

https://cran.r-project.org/web/packages/dual.spls

공식 코드

Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data

https://github.com/05kashyap/AutoEncoded_Image_Transfer

공식 코드

Autoencoded Image Compression for Secure and Fast Transmission

https://github.com/2y7c3/super-resolution-neural-operator

공식 코드

Super-Resolution Neural Operator

https://github.com/AI-secure/DataLens

공식 코드

DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation

https://github.com/AlbertZhangHIT/Hartley-spectral-pooling

공식 코드

Hartley Spectral Pooling for Deep Learning

https://github.com/Aurelien-Pelissier/Measurements-Informatics-on-Hyperspectral-Raman-Images

공식 코드

Beyond the Nucleus: Cytoplasmic Dominance in Follicular Thyroid Carcinoma Detection Using Single-Cell Raman Imaging Across Multiple Devices

https://github.com/BVLC/caffe

공식 코드

Caffe: Convolutional Architecture for Fast Feature Embedding

https://github.com/BangguWu/ECANet

공식 코드

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

https://github.com/BatzoglouLabSU/SIMLR

공식 코드

SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning

https://github.com/BouchardLab/DynamicalComponentsAnalysis

공식 코드

Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis

https://github.com/Caiyq2019/Deep-generative-LDA

공식 코드

Deep generative LDA

https://github.com/CannyLab/tsne-cuda

공식 코드

t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data

https://github.com/Chayadon/Reconstruction_attack_on_DR

공식 코드

Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack

https://github.com/ChengzijunAixiaoli/POTD

공식 코드

Sufficient dimension reduction for classification using principal optimal transport direction

https://github.com/ChengzijunAixiaoli/PPMM

공식 코드

Large-scale optimal transport map estimation using projection pursuit

https://github.com/ChethanKodase/robustness_of_subgroups

공식 코드

Adversarial Robustness of VAEs across Intersectional Subgroups

https://github.com/Christophe-pere/An-encoding-perspective-for-quantum-classification-advantage-using-NISQ-algorithms

공식 코드

A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms

https://github.com/CristianCastiglione/sgdGMF

공식 코드

Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data

https://github.com/CylanceSPEAR/lazy-stochastic-principal-component-analysis

공식 코드

Lazy stochastic principal component analysis

https://github.com/DataSlingers/rho-PCA

공식 코드

Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data

https://github.com/DavidHofmeyr/SCPP

공식 코드

Minimum Spectral Connectivity Projection Pursuit

https://github.com/DesignInformaticsLab/Material-Design

공식 코드

Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design

https://github.com/ETZET/SpeechEmotionAVLearning

공식 코드

Learning Arousal-Valence Representation from Categorical Emotion Labels of Speech

https://github.com/EtienneGof/FunCLBM

공식 코드

Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation

https://github.com/FWen/OUTLR

공식 코드

Efficient Outlier Removal in Large Scale Global Structure-from-Motion

https://github.com/FilippoMB/Bidirectional-Deep-Echo-State-Network

공식 코드

Bidirectional deep-readout echo state networks

https://github.com/FilippoMB/Bidirectional-Deep-reservoir-Echo-State-Network

공식 코드

Bidirectional deep-readout echo state networks

https://github.com/FilippoMB/Reservoir-Computing-framework-for-multivariate-time-series-classification

공식 코드

Reservoir computing approaches for representation and classification of multivariate time series

https://github.com/FilippoMB/Reservoir-model-space-classifier

공식 코드

Reservoir computing approaches for representation and classification of multivariate time series

https://github.com/FluidMechanicsInta/Towards-aerodynamic-surrogate-modeling-based-on-beta-variational-autoencoders

공식 코드

Towards aerodynamic surrogate modeling based on $β$-variational autoencoders

https://github.com/Formsma/GAMA-outliers

공식 코드

In search of the weirdest galaxies in the Universe

https://github.com/FrancescoCrecchi/Multiscale-Parametric-t-SNE

공식 코드

Perplexity-free Parametric t-SNE

https://github.com/GTmac/FastRP

공식 코드

Fast and Accurate Network Embeddings via Very Sparse Random Projection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.