Data Visualization

88 papers with code • 0 benchmarks • 2 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.

Geometry- and Accuracy-Preserving Random Forest Proximities

kevinmoonlab/rf-gap 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.

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.

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

YingfanWang/PaCMAP 8 Dec 2020

In this work, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce.