Dimensionality Reduction
Dimensionality reduction is the task of reducing the dimensionality of a dataset.
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Adversarial Autoencoders
• eriklindernoren/PyTorch-GAN
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In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
#3 best model for
Unsupervised MNIST
on MNIST
DIMENSIONALITY REDUCTION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples.
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years.
Text Classification Algorithms: A Survey
In this paper, a brief overview of text classification algorithms is discussed.
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
ICLR 2019 • JustGlowing/minisom
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
DIMENSIONALITY REDUCTION REPRESENTATION LEARNING TIME SERIES
catch22: CAnonical Time-series CHaracteristics
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry.
DIMENSIONALITY REDUCTION TIME SERIES TIME SERIES ANALYSIS TIME SERIES CLASSIFICATION
Accelerated Stochastic Power Iteration
• noahgolmant/pytorch-hessian-eigenthings
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We propose a simple variant of the power iteration with an added momentum term, that achieves both the optimal sample and iteration complexity.
Deep Continuous Clustering
We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly.
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables.