https://github.com/vwlin/superstar
공식 코드DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models
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
- 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/wanghewei16/video-engagement-analysis
공식 코드DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs
https://github.com/wassmap/wassmap
공식 코드Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning
https://github.com/wassmap/wassmap.github.io
공식 코드Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning
https://github.com/waynchi/gui-agent
공식 코드https://github.com/wdiepeveen/iso-Riemannian-geometry
공식 코드https://github.com/wenwenmin/wspls
공식 코드Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection
https://github.com/westlake-cairi/Markov-Lipschitz-Deep-Learning
공식 코드https://github.com/wielandbrendel/dPCA
공식 코드https://github.com/wiewel/LatentSpacePhysics
공식 코드Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow
https://github.com/will-consagra/emfda
공식 코드Efficient Multidimensional Functional Data Analysis Using Marginal Product Basis Systems
https://github.com/williamgilpin/fnn
공식 코드https://github.com/williamsyy/localmap
공식 코드https://github.com/willtownes/nsf-paper
공식 코드https://github.com/wilsonjr/humap
공식 코드HUMAP: Hierarchical Uniform Manifold Approximation and Projection
https://github.com/wq2012/kPCA
공식 코드Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
https://github.com/wrnmf/robustnmf
공식 코드Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
https://github.com/xcsf-dev/xcsf
공식 코드https://github.com/xianchaoxiu/spca-net
공식 코드Tuning-Free Structured Sparse PCA via Deep Unfolding Networks
https://github.com/xiaosen-wang/TA
공식 코드Triangle Attack: A Query-efficient Decision-based Adversarial Attack
https://github.com/xiaoyangstat/graph-metric
공식 코드A Quotient Space Formulation for Generative Statistical Analysis of Graphical Data
https://github.com/xinxingwu-uk/ufs
공식 코드Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm
https://github.com/xpgogogo/sccdcg
공식 코드https://github.com/xuquanfeng/galaxy_vae
공식 코드https://github.com/xwshen51/distributionalprincipalautoencoder
공식 코드https://github.com/xzhan96-stf/Herbal-medicine-origin-e-nose
공식 코드Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
https://github.com/yabozkurt/gmvae
공식 코드Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
https://github.com/ycq091044/atd
공식 코드https://github.com/yilinye/akrmap
공식 코드AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings
https://github.com/yilinye/modal-fusion-map
공식 코드ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map
https://github.com/yimingsci/distributed-lya-func
공식 코드https://github.com/yingfuu/progunknownfms
공식 코드Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes
https://github.com/ymlasu/camel
공식 코드https://github.com/yoloonme/ema-attention-module
공식 코드Efficient Multi-Scale Attention Module with Cross-Spatial Learning
https://github.com/yongchunli-13/Sparse-PCA
공식 코드https://github.com/ytstat/RaSE-screening-codes
공식 코드RaSE: A Variable Screening Framework via Random Subspace Ensembles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.