Papers with Code 아카이브
Dimensionality Reduction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/vaskarageorg/SCA_MR
https://github.com/vinuni-vishc/pppf-cross-silo-fl
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
https://github.com/vitskvara/GenModels.jl
https://github.com/vitskvara/GenerativeModels.jl
https://github.com/viviencabannes/laplacian
https://github.com/vj-thakur/catboost
https://github.com/vyraun/Half-Size
Simple and Effective Dimensionality Reduction for Word Embeddings
https://github.com/waynezhanghk/gacluster
https://github.com/weallen/STARmap
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/wedeling/EasySurrogate
Deep active subspaces - a scalable method for high-dimensional uncertainty propagation
https://github.com/wenwenmin/SSNMF
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
https://github.com/wilsonjr/ClusterShapley
Explaining dimensionality reduction results using Shapley values
https://github.com/wiradkp/pytorch_speaker_embedding_for_diarization
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/wnstlr/FACT
https://github.com/xiadanqing/Binary
https://github.com/xiaoqian19940510/text-classification-
https://github.com/xmu-xiaoma666/External-Attention-pytorch
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://github.com/xpixelgroup/rdsr
Discovering Distinctive "Semantics" in Super-Resolution Networks
https://github.com/xuyxu/Deep-Clustering-Network
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
https://github.com/yamilvindas/LQ-KNN_DataAnnotation
How to Evaluate Dimensionality Reduction? - Improving the Co-ranking Matrix
https://github.com/yangyucheng000/Paper-4/tree/main/JLONMFSC
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/papercode-2/tree/main/JLONMFSC-master
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yifan-fanyi/Pixelhop
PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification
https://github.com/yifan-fanyi/myMCL-Realization
PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification
https://github.com/yihui-he/exemplar-cnn
Caffe: Convolutional Architecture for Fast Feature Embedding
https://github.com/yinyuan1227/strap-git
https://github.com/yixuan/gradfps
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
https://github.com/yongtaoliu/Decoding-the-shift-invariant-data
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy
https://github.com/yttrilab/b-soid
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/yumoh/catboost_iter
https://github.com/zcemycl/Matlab-GAN
https://github.com/zzmtsvv/adversarial
https://gitlab.com/birder/birder
https://gitlab.com/korovin/iprover
Premise selection with neural networks and distributed representation of features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.