Papers with Code 아카이브

Dimensionality Reduction

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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://github.com/ml-jku/subgd

공식 코드

Few-Shot Learning by Dimensionality Reduction in Gradient Space

https://github.com/mnassar/malware-viz

공식 코드

Throttling Malware Families in 2D

https://github.com/moisesrsantos/featselec

공식 코드

Evaluating Meta-Feature Selection for the Algorithm Recommendation Problem

https://github.com/moldyn/MoSAIC

공식 코드

Correlation-based feature selection to identify functional dynamics in proteins

https://github.com/molex2024/molex

공식 코드

Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property Prediction

https://github.com/molfat66/FairML

공식 코드

Convex Formulations for Fair Principal Component Analysis

https://github.com/molnsona/blossom

공식 코드

Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM

https://github.com/mosco/manifold-learning-arbitrary-norms

공식 코드

Manifold learning with arbitrary norms

https://github.com/mosco/unsupervised-preprocessing

공식 코드

On the cross-validation bias due to unsupervised pre-processing

https://github.com/mrfanuel/sdp-embedding

공식 코드

Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions

https://github.com/muqiao0626/flda-in-computbiol

공식 코드

Factorized Discriminant Analysis for Genetic Signatures of Neuronal Phenotypes

https://github.com/mustafaCoskunAgu/ScNP

공식 코드

Graph Convolutional Networks Meet with High Dimensionality Reduction

https://github.com/mw742/Hillclimb-Causal-Inference

공식 코드

Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in Children

https://github.com/mzalaya/screenkhorn

공식 코드

Screening Sinkhorn Algorithm for Regularized Optimal Transport

https://github.com/nand1155/causnet

공식 코드

CausNet : Generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints

https://github.com/nathanzelesko/earthmover

공식 코드

Earthmover-based manifold learning for analyzing molecular conformation spaces

https://github.com/naver-ai/pit

공식 코드

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/naver/tldr

공식 코드

TLDR: Twin Learning for Dimensionality Reduction

https://github.com/nazeehgh/ModifiedGA

공식 코드

Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

https://github.com/nblt/DLDR

공식 코드

Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces

https://github.com/nblt/twa

공식 코드

Trainable Weight Averaging: A General Approach for Subspace Training

https://github.com/nch08a/EDVizPhenotyping

공식 코드

Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping

https://github.com/neuir/conae

공식 코드

Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder

https://github.com/neurospin-projects/2021_rlouiset_ucsl

공식 코드

UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning

https://github.com/ngmlgroup/koopman-tgnn-interpretability

공식 코드

Interpreting Temporal Graph Neural Networks with Koopman Theory

https://github.com/ngunnar/latent-linear-dynamics-in-spatiotemporal-medical-data

공식 코드

Unsupervised dynamic modeling of medical image transformation

https://github.com/ngunnar/med-dyn-reg

공식 코드

Unsupervised dynamic modeling of medical image transformation

https://github.com/nhuang37/dimensionality_reduction

공식 코드

Dimensionality reduction, regularization, and generalization in overparameterized regressions

https://github.com/nikpocuca/MatrixVariate.jl

공식 코드

Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers

https://github.com/njohnson99/spotcheck

공식 코드

Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models

https://github.com/njustesen/a2c_gvgai

공식 코드

Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation

https://github.com/nla-group/fabba

공식 코드

An efficient aggregation method for the symbolic representation of temporal data

https://github.com/nmank/FD

공식 코드

A Flag Decomposition for Hierarchical Datasets

https://github.com/nmank/funwithflags

공식 코드

Fun with Flags: Robust Principal Directions via Flag Manifolds

https://github.com/nmucke/data-assimilation

공식 코드

The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification

https://github.com/nobuo-namura/regional-expected-improvement

공식 코드

Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization

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