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/flpmasi/tann-multiscale

공식 코드

Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)

https://github.com/flyingdoog/DMGC

공식 코드

Spectral Clustering of Attributed Multi-relational Graphs

https://github.com/foger3/arc_deeplearning

공식 코드

Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method

https://github.com/fpaulovich/dimensionality-reduction

공식 코드

A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts

https://github.com/francescodisalvo05/wann-noisy-labels

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An Embedding is Worth a Thousand Noisy Labels

https://github.com/fraunhofer-aisec/quack

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QUACK: Quantum Aligned Centroid Kernel

https://github.com/furkanyesiler/re-move

공식 코드

Less is more: Faster and better music version identification with embedding distillation

https://github.com/ganguli-lab/deep-retina-reduction

공식 코드

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

https://github.com/geomstats/geomstats

공식 코드

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

https://github.com/ginagruenhage/cmdsr

공식 코드

Visualizing the Effects of a Changing Distance on Data Using Continuous Embeddings

https://github.com/gjhuizing/wsingular

공식 코드

Unsupervised Ground Metric Learning using Wasserstein Singular Vectors

https://github.com/gkpapers/2021aggregatemca

공식 코드

Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

https://github.com/gladia-research-group/latent-autoregressive-source-separation

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Latent Autoregressive Source Separation

https://github.com/gnodking7/wdanepv

공식 코드

A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis

https://github.com/google-research/google-research

공식 코드

Scalable Differentially Private Clustering via Hierarchically Separated Trees

https://github.com/gregversteeg/LinearSieve

공식 코드

Sifting Common Information from Many Variables

https://github.com/gudovskiy/fmap_compression

공식 코드

DNN Feature Map Compression using Learned Representation over GF(2)

https://github.com/guilhermepelegrina/fpca

공식 코드

A novel approach for Fair Principal Component Analysis based on eigendecomposition

https://github.com/gwgundersen/rflvm

공식 코드

Latent variable modeling with random features

https://github.com/gyyang/nn-brain

공식 코드

Modular Boundaries in Recurrent Neural Networks

https://github.com/halquabeh/llm_natural_log

공식 코드

Number Representations in LLMs: A Computational Parallel to Human Perception

https://github.com/hankye/once-for-both

공식 코드

Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression

https://github.com/hanlu-nju/augca

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Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps

https://github.com/harlinlee/psc

공식 코드

$O(k)$-Equivariant Dimensionality Reduction on Stiefel Manifolds

https://github.com/hci-unihd/DTAE

공식 코드

Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder

https://github.com/helmuthn/naumer_dimensionality_2022.jl

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Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon Forecasting

https://github.com/hewell0/projood

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Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer

https://github.com/hgeno/WFLSNGCA

공식 코드

Whitening-Free Least-Squares Non-Gaussian Component Analysis

https://github.com/higeeks/fgspca

공식 코드

Feature Grouping and Sparse Principal Component Analysis with Truncated Regularization

https://github.com/hippylib/hippyflow

공식 코드

Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning

https://github.com/hit-leaderone/filp-3d

공식 코드

FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models

https://github.com/hit-leaderone/flip-3d

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FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models

https://github.com/hj-n/ltnc

공식 코드

Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction

https://github.com/hj-n/zadu

공식 코드

ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings

https://github.com/hku-mars/immesh

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ImMesh: An Immediate LiDAR Localization and Meshing Framework

https://github.com/hkuedl/Latent-Variable-based-Optimization

공식 코드

Dimension-reduced Optimization of Multi-zone Thermostatically Controlled Loads

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