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Dimensionality Reduction

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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://github.com/javiersc1/alpcahus

공식 코드

ALPCAHUS: Subspace Clustering for Heteroscedastic Data

https://github.com/jayroxis/dam-pytorch

공식 코드

Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)

https://github.com/jayroxis/ordering-in-graph-generation

공식 코드

Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation

https://github.com/jbramburger/Deep-Conjugacies

공식 코드

Deep Learning of Conjugate Mappings

https://github.com/jcl823/maw

공식 코드

Novelty Detection via Robust Variational Autoencoding

https://github.com/jeroenbe/ebm-for-cate

공식 코드

Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects

https://github.com/jerrysoybean/ikd

공식 코드

Inverse Kernel Decomposition

https://github.com/jgeofil/mycorrhiza-algorithm

공식 코드

Mycorrhiza: Genotype Assignment usingPhylogenetic Networks

https://github.com/jichao1/wgan-gp

공식 코드

Subsurface Characterization using Ensemble-based Approaches with Deep Generative Models

https://github.com/jieyu97/invariantVAE

공식 코드

Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods

https://github.com/jintangxue/wordfs

공식 코드

Word Embedding Dimension Reduction via Weakly-Supervised Feature Selection

https://github.com/jjbouza/manifold-net

공식 코드

ManifoldNet: A Deep Network Framework for Manifold-valued Data

https://github.com/jkneifl/VENI-VINDy-VICI

공식 코드

VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification

https://github.com/jku-vds-lab/paradime

공식 코드

ParaDime: A Framework for Parametric Dimensionality Reduction

https://github.com/jlmelville/uwot

공식 코드

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

https://github.com/johannbrehmer/manifold-flow

공식 코드

Flows for simultaneous manifold learning and density estimation

https://github.com/johertrich/PCA_GMMs

공식 코드

PCA Reduced Gaussian Mixture Models with Applications in Superresolution

https://github.com/johnnellas/csae

공식 코드

Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders

https://github.com/jshyou/Restricted-Boltzmann-Machine

공식 코드

Data Mapping and Finite Difference Learning

https://github.com/juliabringewald/Solar-Flare-Forecast

공식 코드

Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class Prediction

https://github.com/juliagni/geometricmachinelearning.jl

공식 코드

Symplectic Autoencoders for Model Reduction of Hamiltonian Systems

https://github.com/juliusaka/balanced-neural-odes

공식 코드

Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations

https://github.com/junegoo94/diffusion_maps

공식 코드

Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps

https://github.com/junjun-jiang/SuperPCA

공식 코드

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

https://github.com/justinmlin/dr-framework

공식 코드

Calibrating dimension reduction hyperparameters in the presence of noise

https://github.com/juyongjiang/vfedpca-vfedakpca

공식 코드

Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data

https://github.com/jwyyy/AdaFNN

공식 코드

Deep Learning for Functional Data Analysis with Adaptive Basis Layers

https://github.com/k-nic/alternating-scheme

공식 코드

Reducing the dimensionality of data using tempered distributions

https://github.com/kaimihata/geo-bert

공식 코드

On Geodesic Distances and Contextual Embedding Compression for Text Classification

https://github.com/kamilazdybal/local-manifold-learning

공식 코드

Local manifold learning and its link to domain-based physics knowledge

https://github.com/kasparmartens/BasisVAE

공식 코드

BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders

https://github.com/kasparmartens/NeuralDecomposition

공식 코드

Neural Decomposition: Functional ANOVA with Variational Autoencoders

https://github.com/kasparmartens/c-GPLVM

공식 코드

Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

https://github.com/katiana22/GDM-PCE

공식 코드

Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models

https://github.com/kaversoniano/gpcca

공식 코드

Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

https://github.com/kazkara/adept

공식 코드

ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning

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