Data Visualization

64 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Adversarial Autoencoders

eriklindernoren/PyTorch-GAN 18 Nov 2015

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.

ShapeNet: An Information-Rich 3D Model Repository

tensorflow/models 9 Dec 2015

We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects.

A high-bias, low-variance introduction to Machine Learning for physicists

drckf/mlreview_notebooks 23 Mar 2018

The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists.

Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks

victordibia/data2vis 9 Apr 2018

Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization.

Kernelized Synaptic Weight Matrices

lorenzMuller/kernelNet_MovieLens ICML 2018

In this paper we introduce a novel neural network architecture, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions.

Torchbearer: A Model Fitting Library for PyTorch

ecs-vlc/torchbearer 10 Sep 2018

We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming.

Geometry- and Accuracy-Preserving Random Forest Proximities

jakerhodes/RF-GAP-Python 29 Jan 2022

Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning.

Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment

gitr00ki3/vpw 7 Dec 2002

Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications.