Data Visualization
43 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Data Visualization
MIMI dataset
Most implemented papers
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.
ShapeNet: An Information-Rich 3D Model Repository
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
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
Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization.
A new robust feature selection method using variance-based sensitivity analysis
Excluding irrelevant features in a pattern recognition task plays an important role in maintaining a simpler machine learning model and optimizing the computational efficiency.
Torchbearer: A Model Fitting Library for PyTorch
We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming.
Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment
Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications.
Doubly Stochastic Neighbor Embedding on Spheres
To solve this problem, we introduce a fast normalization method and normalize the similarity matrix to be doubly stochastic such that all the data points have equal total similarities.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
uber-common/deep-neuroevolution
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Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems.
Kernelized Synaptic Weight Matrices
lorenzMuller/kernelNet_MovieLens
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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.
