Papers with Code 아카이브

Dimensionality Reduction

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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/hrbigelow/ae-wavenet

Unsupervised speech representation learning using WaveNet autoencoders

https://github.com/hsmaan/CovidGenotyper

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

https://github.com/huguyuehuhu/fasttsne

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

https://github.com/human-analysis/secure-face-matching

Secure Face Matching Using Fully Homomorphic Encryption

https://github.com/husun0822/BlueSky_Project

Interpreting LSTM Prediction on Solar Flare Eruption with Time-series Clustering

https://github.com/hwalsuklee/tensorflow-mnist-AAE

Adversarial Autoencoders

https://github.com/i13abe/qSNE

q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor Embedding

https://github.com/i13abe/q_SNE

q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor Embedding

https://github.com/ibm/fungcn

A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spaces

https://github.com/ilyak93/DCC

Deep Continuous Clustering

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

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

https://github.com/isarandi/metrabs

Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats

https://github.com/isarandi/posepile

Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats

https://github.com/ishspsy/project

Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

https://github.com/jameschapman19/cca_zoo

Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction

https://github.com/jameschapman19/cca_zoo/blob/main/cca_zoo/deepmodels/_discriminative/_dcca_eigengame.py

A Generalized EigenGame with Extensions to Multiview Representation Learning

https://github.com/jeija/espargos-wifi-channelcharting

WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels

https://github.com/jesusdaniel/JEG

Joint Embedding of Graphs

https://github.com/jiangzhongkai/ifly-algorithm_challenge

XGBoost: A Scalable Tree Boosting System

https://github.com/jianhao2016/online_cvxMF

Online Convex Matrix Factorization with Representative Regions

https://github.com/jlacombe/learning-to-generate-wasserstein-barycenters

Learning Wasserstein Embeddings

https://github.com/jlanday/Language-Detection

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/X-ray-Object-Classification

XGBoost: A Scalable Tree Boosting System

https://github.com/joezengcbs/mini_xgboost_from_scratch

XGBoost: A Scalable Tree Boosting System

https://github.com/joneswack/dp-rfs

The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings

https://github.com/jorisvandenbossche/target-encoder-benchmarks

Similarity encoding for learning with dirty categorical variables

https://github.com/jos139/adversarial_autoencoder

Adversarial Autoencoders

https://github.com/jprost76/AAE-Pytorch

Adversarial Autoencoders

https://github.com/justin941208/MPhil-Thesis

Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections

https://github.com/katiana22/GDM-PCE-surrogates

Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models

https://github.com/kazeevn/catboost

CatBoost: unbiased boosting with categorical features

https://github.com/kbountro/cSAX

Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection

https://github.com/keil555/AAE_paddle

Adversarial Autoencoders

https://github.com/klugerlab/FIt-SNE

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

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