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/NJ-Murphy/Learning-Technical-Trading

공식 코드

Learning the dynamics of technical trading strategies

https://github.com/Neurology-AI-Program/sparca

공식 코드

SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction

https://github.com/OriKatz/SpectralFlowAnalysis

공식 코드

Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing

https://github.com/Otsuzuki/Meta-learning-of-Pooling-Layers-for-Character-Recognition

공식 코드

Meta-learning of Pooling Layers for Character Recognition

https://github.com/PaoloBonettiPolimi/NonLinCTFA

공식 코드

Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis

https://github.com/PerForm-Lab-RIT/domain-adaptation-eye-tracking

공식 코드

Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems

https://github.com/PriyeshV/NRL_Benchmark

공식 코드

Network Representation Learning: Consolidation and Renewed Bearing

https://github.com/RonMcKay/OODRetrieval

공식 코드

Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation

https://github.com/SDPforAll/multiCriteriaDimReduction

공식 코드

Multi-Criteria Dimensionality Reduction with Applications to Fairness

https://github.com/SMART-Research/leaffeatures_paper

공식 코드

Computer-aided Interpretable Features for Leaf Image Classification

https://github.com/SMTorg/SMT

공식 코드

High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft

https://github.com/SPEECHCOG/cpc_pytorch

공식 코드

Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset

공식 코드

Deep Learning of Individual Aesthetics

https://github.com/Slowika/Bag-of-Recipes

공식 코드

Completing partial recipes using item-based collaborative filtering to recommend ingredients

https://github.com/StavGer/IMAPCE

공식 코드

Cluster Exploration using Informative Manifold Projections

https://github.com/StevenGolovkine/FDApy

공식 코드

FDApy: a Python package for functional data

https://github.com/Tommoo/FinslerMDS

공식 코드

Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding

https://github.com/TrustAI/DeepConcolic

공식 코드

Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features

https://github.com/Tveten/tdpcaTEP

공식 코드

Online Detection of Sparse Changes in High-Dimensional Data Streams Using Tailored Projections

https://github.com/Tveten/tpcaMonitoring

공식 코드

Online Detection of Sparse Changes in High-Dimensional Data Streams Using Tailored Projections

https://github.com/UniFeat/unifeat

공식 코드

Universal Feature Selection Tool (UniFeat): An Open-Source Tool for Dimensionality Reduction

https://github.com/VityaVitalich/MultiModalCoreset

공식 코드

Challenges of Multi-Modal Coreset Selection for Depth Prediction

https://github.com/WenjieDu/PyPOTS

공식 코드

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

https://github.com/Wenxing99/RPEDR

공식 코드

Random-projection ensemble dimension reduction

https://github.com/Westlake-AI/inv-ML

공식 코드

Invertible Manifold Learning for Dimension Reduction

https://github.com/Westlake-AI/openmixup

공식 코드

GenURL: A General Framework for Unsupervised Representation Learning

https://github.com/XJhaoren/IME_layer

공식 코드

Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval

https://github.com/XavierOwen/ALC-AEGP

공식 코드

Active Learning for Manifold Gaussian Process Regression

https://github.com/XiangLi1999/syntactic-VIB

공식 코드

Specializing Word Embeddings (for Parsing) by Information Bottleneck

https://github.com/Xiaohu-Zheng/Deep-aPCE

공식 코드

Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification

https://github.com/Xieyangxinyu/TrIM

공식 코드

TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression

https://github.com/XuFengthucs/frPCA_sparse

공식 코드

Fast Randomized PCA for Sparse Data

https://github.com/YingfanWang/PaCMAP

공식 코드

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

https://github.com/ZhenhuaShi/SDSPCAAN

공식 코드

Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction

https://github.com/ZilongTan/fgp

공식 코드

Learning Fair Representations for Kernel Models

https://github.com/ZilongTan/sigp

공식 코드

Learning Integral Representations of Gaussian Processes

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