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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/tamirbendory/cryoemsignalenhancement

공식 코드

Signal enhancement for two-dimensional cryo-EM data processing

https://github.com/tanpei0513/vicsek_trajectory

공식 코드

Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reduction

https://github.com/taralloc/deepkpca

공식 코드

Deep Kernel Principal Component Analysis for Multi-level Feature Learning

https://github.com/tariqul-islam/explaining_neighbor_embedding

공식 코드

The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction

https://github.com/tdavislab/verb

공식 코드

VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations

https://github.com/tencent-ailab/UltraDualPathCompression

공식 코드

Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression

https://github.com/tengandreaxu/benign-autoencoders

공식 코드

Benign Autoencoders

https://github.com/tensorflow/models

공식 코드

Unifying Deep Local and Global Features for Image Search

https://github.com/tensorflow/tensorflow

공식 코드

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

https://github.com/thanatorika/mrtg

공식 코드

Latent Manifold Reconstruction and Representation with Topological and Geometrical Regularization

https://github.com/theorod93/multiSNE

공식 코드

Multi-view Data Visualisation via Manifold Learning

https://github.com/theorod93/multiView_manifoldLearning

공식 코드

Multi-view Data Visualisation via Manifold Learning

https://github.com/tianran/glimvec

공식 코드

Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder

https://github.com/tillahoffmann/summaries

공식 코드

Unifying Summary Statistic Selection for Approximate Bayesian Computation

https://github.com/timsainb/ParametricUMAP_paper

공식 코드

Parametric UMAP embeddings for representation and semi-supervised learning

https://github.com/tinachentc/kernel-learning-in-ridge-regression

공식 코드

Layered Models can "Automatically" Regularize and Discover Low-Dimensional Structures via Feature Learning

https://github.com/tobc/dartminhash

공식 코드

DartMinHash: Fast Sketching for Weighted Sets

https://github.com/tommikark/additiveautoencoder

공식 코드

An Additive Autoencoder for Dimension Estimation

https://github.com/tomoleary/dino

공식 코드

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

https://github.com/tsourolampis/Adagio

공식 코드

ADAGIO: Fast Data-aware Near-Isometric Linear Embeddings

https://github.com/ucsb-casl/curvature_ecnet_2d

공식 코드

Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set Method

https://github.com/ucsb-casl/curvature_ecnet_3d

공식 코드

Machine learning algorithms for three-dimensional mean-curvature computation in the level-set method

https://github.com/unihd-ceg/deephydra

공식 코드

DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems

https://github.com/uriamorp/mprod_package

공식 코드

Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization

https://github.com/uthaipon/multi-criteria-dimensionality-reduction

공식 코드

Multi-Criteria Dimensionality Reduction with Applications to Fairness

https://github.com/utsav-akhaury/gprMax

공식 코드

Machine Learning Based Forward Solver: An Automatic Framework in gprMax

https://github.com/uwjunqi/ttn-vqc

공식 코드

Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression

https://github.com/vadicamo/nsimplex

공식 코드

nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces

https://github.com/vafaei-ar/drama

공식 코드

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

https://github.com/valentyn1997/ricb

공식 코드

Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation

https://github.com/vda-lab/lensed_umap

공식 코드

Lens functions for exploring UMAP Projections with Domain Knowledge

https://github.com/vinayaksodar/nlp_drift_paper_code

공식 코드

Detecting covariate drift in text data using document embeddings and dimensionality reduction

https://github.com/visinf/fldr-vfi

공식 코드

Efficient Feature Extraction for High-resolution Video Frame Interpolation

https://github.com/vleplat/ntd-algorithms

공식 코드

Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models

https://github.com/vrd1243/tda_bees

공식 코드

A Computational Topology-based Spatiotemporal Analysis Technique for Honeybee Aggregation

https://github.com/vveitch/causal-text-embeddings-tf2

공식 코드

Adapting Text Embeddings for Causal Inference

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