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Data Visualization

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in data-visualization-5

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Libraries

i

Use these libraries to find data-visualization-5 models and implementations

Datasets

MIMI dataset

PJM(AEP)

Subtasks

Tree Map Layout

Most implemented papers

Adversarial Autoencoders

I. Goodfellow, Jonathon Shlens, N. Jaitly, Alireza Makhzani•Tue Nov 17 2015

It is shown how the adversarial autoencoder can be used in applications such as semi-supervised classification, disentangling style and content of images, unsupervised clustering, dimensionality reduction and data visualization.

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ShapeNet: An Information-Rich 3D Model Repository

Hao Su, L. Guibas, L. Yi, S. Savarese, Jianxiong Xiao, Shuran Song, T. Funkhouser, F. Yu, Angel X. Chang, M. Savva, P. Hanrahan, Qi-Xing Huang, Zimo Li•Tue Dec 08 2015

ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy, a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations.

6062 0

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

Pankaj Mehta, D. Schwab, M. Bukov, Ching-Hao Wang, A. Day, C. Richardson, Charles K. Fisher•Thu Mar 22 2018

The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning.

955 0

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Geometry- and Accuracy-Preserving Random Forest Proximities

Jake S. Rhodes, Adele Cutler, Kevin R. Moon•Fri Jan 28 2022

This paper proves that the proximity-weighted sum (regression) or majority vote (classification) using RF-GAP exactly matches the out-of-bag random forest prediction, thus capturing the data geometry learned by the random forest.

133 0

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

Victor C. Dibia, Çağatay Demiralp•Sun Apr 08 2018

Data2Vis is introduced, an end-to-end trainable neural translation model for automatically generating visualizations from given datasets that are comparable to manually created visualizations in a fraction of the time, with potential to learn more complex visualization strategies at scale.

214 0

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Kernelized Synaptic Weight Matrices

Julien N. P. Martel, G. Indiveri, Lorenz K. Muller•Mon Jul 02 2018

A novel neural network architecture is introduced, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions, exploring how it can be used to impose structure on neural networks in diverse applications ranging from data visualization to recommender systems.

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Torchbearer: A Model Fitting Library for PyTorch

Jonathon S. Hare, Ethan Harris, Matthew Painter•Sun Sep 09 2018

We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming. The torchbearer library provides a high level metric and callback API that can be used for a wide range of applications. We also include a series of built in callbacks that can be used for: model persistence, learning rate decay, logging, data visualization and more. The extensive documentation includes an example library for deep learning and dynamic programming problems and can be found at this http URL The code is licensed under the MIT License and available at https://github.com/ecs-vlc/torchbearer.

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

C. Rudin, Haiyang Huang, Yingfan Wang, Yaron Shaposhnik•Mon Dec 07 2020

This work provides several unexpected insights into what design choices to make and avoid when constructing DR algorithms, and designs a new algorithm, called Pairwise Controlled Manifold Approximation Projection (PaCMAP), which preserves both local and global structure.

435 0

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Branch and End Points Detection in Cerebral Vessels Images Using Deep Learning Object Detection Techniques

Samet Kaya, Berna Kiraz, A. Çamurcu•Tue Sep 03 2024

This study introduces a cutting-edge methodology for detecting branching and endpoints in two-dimensional brain vessel images, employing deep learning-based object detection techniques, and underscores the robust performance of the deep learning approach.

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