Papers with Code 아카이브
Dimensionality Reduction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/kwantommy/breast-cancer-diagnosis
https://github.com/layer6ai-labs/calo-man
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
https://github.com/layer6ai-labs/dgm_manifold_survey
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
https://github.com/layer6ai-labs/rectangular-flows
Flows for simultaneous manifold learning and density estimation
https://github.com/layer6ai-labs/uomh
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
https://github.com/leonoravesterbacka/carl-torch
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
https://github.com/liangbright/OOD_Attack_NN
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
https://github.com/lightonai/supervised-random-projections
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections
https://github.com/lij131/LatentSpacePhysics
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow
https://github.com/lijunsun/bgcp_imputation
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion
https://github.com/linqinghong/Deep-Clustering-Paper
https://github.com/lockef/bidifac
Integrative Factorization of Bidimensionally Linked Matrices
https://github.com/lrthomps/umap_var
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/luca-parisi/m-arcsinh_scikit-learn_TensorFlow_Keras
https://github.com/luca-parisi/m_arcsinh_scikit_learn
https://github.com/lucasmfaria/Machine-Learning-Capstone
https://github.com/lukashedegaard/ride
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/lvyufeng/MindSpore-GAN
https://github.com/lxiao0620/gradfps
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
https://github.com/machenslab/dPCA
https://github.com/mahmad00/Artifacts-of-DR-on-Hybrid-CNN-for-HSIC
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
https://github.com/maikejulie/MLINT
Objective discovery of dominant dynamical processes with intelligible machine learning
https://github.com/markWJJ/Multitask-Learning
https://github.com/martinepalazzo/biomed_dim_red_thesis
Latent regularization for feature selection using kernel methods in tumor classification
https://github.com/martinsbruveris/tensorflow-image-models
https://github.com/mcsorkun/ChemPlot
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/mdcnn/mdcnn.github.io
Virtual Codec Supervised Re-Sampling Network for Image Compression
https://github.com/mdozmorov/scRNA-seq_notes
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/meghshukla/LEt-SNE
LEt-SNE: A Hybrid Approach To Data Embedding and Visualization of Hyperspectral Imagery
https://github.com/merchen911/SOM-VAE
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
https://github.com/mevers/animated_dimensionality_reduction
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/ECAAttention.py
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/PIT.py
https://github.com/mmukhopadhyay/-Efficient-Manifold-Learning-Using-Spherelets
Efficient Manifold and Subspace Approximations with Spherelets
https://github.com/mnikitin/channel-attention
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.