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/yuqinie98/patchtst

공식 코드

Revisiting PCA for time series reduction in temporal dimension

https://github.com/yuroeth/icu_benchmarks

공식 코드

Language Model Training Paradigms for Clinical Feature Embeddings

https://github.com/yurongling/DR-for-Data-with-Missingness

공식 코드

Dimension Reduction for Data with Heterogeneous Missingness

https://github.com/yuyolshin/MW-TGC-PyTorch

공식 코드

Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting

https://github.com/yzhao062/SUOD

공식 코드

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

https://github.com/yzhao98/epr-net

공식 코드

EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation

https://github.com/zahraatashgahi/QuickSelection

공식 코드

Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

https://github.com/zangzelin/code_EVNet

공식 코드

EVNet: An Explainable Deep Network for Dimension Reduction

https://github.com/zangzelin/code_dmthi

공식 코드

DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality Reduction

https://github.com/zboyd2/hitting_probabilities_metric

공식 코드

A metric on directed graphs and Markov chains based on hitting probabilities

https://github.com/zera/Nips_MT

공식 코드

Practical Hash Functions for Similarity Estimation and Dimensionality Reduction

https://github.com/zhang-guangyi/semantic-mdjcm

공식 코드

From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic Communication

https://github.com/zhangaipi/dim

공식 코드

Discover and Mitigate Multiple Biased Subgroups in Image Classifiers

https://github.com/zhangrh93/InvertibleCE

공식 코드

Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors

https://github.com/zhangxiaoyu11/OmiEmbed

공식 코드

OmiEmbed: a unified multi-task deep learning framework for multi-omics data

https://github.com/zhaoyj21/fcg

공식 코드

Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting

https://github.com/zhemingzuo/CFS

공식 코드

Curvature-based Feature Selection with Application in Classifying Electronic Health Records

https://github.com/zhidilin/advisedgplvm

공식 코드

Preventing Model Collapse in Gaussian Process Latent Variable Models

https://github.com/ziyuw/rembo

공식 코드

High Dimensional Bayesian Optimization via Supervised Dimension Reduction

https://github.com/zjj20212035/spca-psd

공식 코드

Bi-Sparse Unsupervised Feature Selection

https://github.com/zod-l/ctsae

공식 코드

Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave Glitches

https://github.com/zoharri/mbrl2

공식 코드

Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation Approach

https://github.com/zouharvi/kb-shrink

공식 코드

Knowledge Base Index Compression via Dimensionality and Precision Reduction

https://github.com/zukang-liao/context-sensitive-image-similarity

공식 코드

https://gitlab.com/SCoRe-Group/IsomapSpark

공식 코드

Scalable Manifold Learning for Big Data with Apache Spark

https://gitlab.com/bardhp95/bae

공식 코드

Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection

https://gitlab.com/drti/mmgp

공식 코드

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability

https://gitlab.com/hararticles/orthovar

공식 코드

On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems

https://gitlab.com/jubrau/lbd

공식 코드

Convergent autoencoder approximation of low bending and low distortion manifold embeddings

https://gitlab.com/nicolasevangelou/colloidal-assembly-learning-sdes

공식 코드

Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles

https://gitlab.com/polavieja_lab/cnn-lda

공식 코드

Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features

https://gitlab.com/raphaelgautier/bayesian-supervised-dimension-reduction

공식 코드

A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes

https://gitlab.com/robinforsling/dtt

공식 코드

Decentralized State Estimation In A Dimension-Reduced Linear Regression

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