Papers with Code 아카이브

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/cgshpi/topic-models-and-dimensionality-reduction-benchmark

공식 코드

Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization

https://github.com/chanshing/geocondition

공식 코드

Parametric generation of conditional geological realizations using generative neural networks

https://github.com/charlie-xiao/embedding-visualization-test

공식 코드

Efficiently Visualizing Large Graphs

https://github.com/chengs94/RapPCA/tree/main/cleaned_code_for_review

공식 코드

Principal component analysis balancing prediction and approximation accuracy for spatial data

https://github.com/chenwei-zhang/ViDa

공식 코드

Visualizing DNA reaction trajectories with deep graph embedding approaches

https://github.com/chlubba/catch22

공식 코드

catch22: CAnonical Time-series CHaracteristics

https://github.com/chlubba/op_importance

공식 코드

catch22: CAnonical Time-series CHaracteristics

https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model

공식 코드

A Survey on Generative Diffusion Model

https://github.com/cics-nd/sgplvm-inverse

공식 코드

Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion

https://github.com/cjfcsjt/SILBO

공식 코드

Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization

https://github.com/cod3licious/simec

공식 코드

Predicting Pairwise Relations with Neural Similarity Encoders

https://github.com/collection-space-navigator/csn

공식 코드

Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets

https://github.com/compstorylab/sirius

공식 코드

Sirius: Visualization of Mixed Features as a Mutual Information Network Graph

https://github.com/comptop/interleaving-dr

공식 코드

Topology-Preserving Dimensionality Reduction via Interleaving Optimization

https://github.com/contipaolo/multifidelity_pod

공식 코드

Multi-fidelity reduced-order surrogate modeling

https://github.com/crispfish/psc

공식 코드

$O(k)$-Equivariant Dimensionality Reduction on Stiefel Manifolds

https://github.com/crispitagorico/SignatureKernel

공식 코드

The Signature Kernel is the solution of a Goursat PDE

https://github.com/crispitagorico/sigkernel

공식 코드

The Signature Kernel is the solution of a Goursat PDE

https://github.com/cruiseresearchgroup/Disentangled_ST_VAE

공식 코드

Measuring disentangled generative spatio-temporal representation

https://github.com/cunni/ldr

공식 코드

Linear Dimensionality Reduction: Survey, Insights, and Generalizations

https://github.com/cunningham-lab/drs

공식 코드

Deep Random Splines for Point Process Intensity Estimation of Neural Population Data

https://github.com/cvgmi/nestedgrassmann

공식 코드

Nested Grassmannians for Dimensionality Reduction with Applications

https://github.com/cvxgrp/pymde

공식 코드

Minimum-Distortion Embedding

https://github.com/dalya/Sequencer

공식 코드

Extracting the main trend in a dataset: the Sequencer algorithm

https://github.com/danchern97/rtd_ae

공식 코드

Learning Topology-Preserving Data Representations

https://github.com/danfenghong/ECCV2018_J-Play

공식 코드

Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction

https://github.com/dangpnh2/plsv_vae

공식 코드

Auto-Encoding Variational Bayes for Inferring Topics and Visualization

https://github.com/danisson/UnscentedGPLVM

공식 코드

Learning GPLVM with arbitrary kernels using the unscented transformation

https://github.com/dannyzx/gaussian-rbfnn

공식 코드

Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks

https://github.com/dannyzx/grbf-nns

공식 코드

Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks

https://github.com/davidaristoff/fkmd

공식 코드

Featurizing Koopman Mode Decomposition For Robust Forecasting

https://github.com/deepintostreams/gcn-devlstm

공식 코드

GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition

https://github.com/dherrera1911/sqfa

공식 코드

Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction

https://github.com/diana-hep/carl

공식 코드

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

https://github.com/diarra2339/pac-bayes-wgan

공식 코드

PAC-Bayesian Generalization Bounds for Adversarial Generative Models

https://github.com/diffeqml/kairos

공식 코드

Transform Once: Efficient Operator Learning in Frequency Domain

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