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/hoangthangta/all-kan

공식 코드

PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks

https://github.com/hongshuochen/DefakeHop

공식 코드

DefakeHop: A Light-Weight High-Performance Deepfake Detector

https://github.com/hozumiyu/ccp-scrnaseq-umap-tsne

공식 코드

Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE

https://github.com/hpicgs/topic-models-and-dimensionality-reduction-sensitivity-study

공식 코드

A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

https://github.com/hrluo/sphericalrotationdimensionreduction

공식 코드

Spherical Rotation Dimension Reduction with Geometric Loss Functions

https://github.com/huanzhang12/ZOO-Attack

공식 코드

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/hubiodatalab/selformer

공식 코드

SELFormer: Molecular Representation Learning via SELFIES Language Models

https://github.com/human-analysis/encrypted-biometric-fusion

공식 코드

HEFT: Homomorphically Encrypted Fusion of Biometric Templates

https://github.com/hyhuang00/paramrepulsor

공식 코드

Navigating the Effect of Parametrization for Dimensionality Reduction

https://github.com/hylu1994/network-cv

공식 코드

Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction

https://github.com/hyungkwonko/umato

공식 코드

Uniform Manifold Approximation with Two-phase Optimization

https://github.com/hzheng40/objective_space_cfr

공식 코드

Game-theoretic Objective Space Planning

https://github.com/iamhuijben/som-cpc

공식 코드

SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series

https://github.com/ichakra2/pca-hybrid

공식 코드

Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence

https://github.com/ichittumuri/spe_journal_risk_analysis_of_flowlines

공식 코드

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach

https://github.com/ikiga1/qadra

공식 코드

Quantum algorithms for SVD-based data representation and analysis

https://github.com/imics-lab/tts-gan

공식 코드

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network

https://github.com/immi000/chemicalneuralode

공식 코드

Speeding up astrochemical reaction networks with autoencoders and neural ODEs

https://github.com/imperialcollegelondon/gsvgd

공식 코드

Grassmann Stein Variational Gradient Descent

https://github.com/imyizhang/manifold-learn

공식 코드

Supervised Stochastic Neighbor Embedding Using Contrastive Learning

https://github.com/infprobscix/gplvm_scrna

공식 코드

Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs

https://github.com/inm-6/sas

공식 코드

Assessing the similarity of real matrices with arbitrary shape

https://github.com/intellabs/vectorsearchdatasets

공식 코드

LeanVec: Searching vectors faster by making them fit

https://github.com/intelligent-exploration/IP_MinEx

공식 코드

Computer vision-based framework for extracting geological lineaments from optical remote sensing data

https://github.com/irinagain/DAP

공식 코드

Sparse quadratic classification rules via linear dimension reduction

https://github.com/irishryoon/analogous_neural

공식 코드

Tracking the topology of neural manifolds across populations

https://github.com/jacobwang925/path-integral-pinn

공식 코드

Physics-Informed Representation and Learning: Control and Risk Quantification

https://github.com/jacoxu/STC2

공식 코드

Self-Taught Convolutional Neural Networks for Short Text Clustering

https://github.com/jaekyeom/drop-bottleneck

공식 코드

Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration

https://github.com/jain15mayank/Clustering-Framework-for-Resedential-Demand-Profiles

공식 코드

A Clustering Framework for Residential Electric Demand Profiles

https://github.com/jain15mayank/validating-clustering-frameworks

공식 코드

Validating Clustering Frameworks for Electric Load Demand Profiles

https://github.com/jamartinh/polcanet

공식 코드

Principal Orthogonal Latent Components Analysis (POLCA Net)

https://github.com/jaromhogue/kdlayers

공식 코드

Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures

https://github.com/jasontslxd/pqwgan

공식 코드

Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation

https://github.com/javier-pg/dark-web-architecture

공식 코드

A Big Data Architecture for Early Identification and Categorization of Dark Web Sites

https://github.com/javiersc1/alpcah

공식 코드

ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization

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