Data Visualization
87 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Data Visualization
MIMI dataset
PJM(AEP)
Latest papers
LineFormer: Rethinking Line Chart Data Extraction as Instance Segmentation
Existing works, however, are not robust to all these variations, either taking an all-chart unified approach or relying on auxiliary information such as legends for line data extraction.
Ellipsoid fitting with the Cayley transform
We introduce Cayley transform ellipsoid fitting (CTEF), an algorithm that uses the Cayley transform to fit ellipsoids to noisy data in any dimension.
NeuroDAVIS: A neural network model for data visualization
For the biological datasets, besides t-SNE, UMAP and Fit-SNE, NeuroDAVIS has also performed well compared to other state-of-the-art algorithms, like Potential of Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) and the siamese neural network-based method, called IVIS.
Multi-task Meta Label Correction for Time Series Prediction
senyuanya/mmlc
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To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework.
Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability
We compare the performance of xGPR with the reported performance of various deep learning models on 20 benchmarks, including small molecule, protein sequence and tabular data.
ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.
A general framework for implementing distances for categorical variables
For categorical data, the definition of a distance is more complex, as there is no straightforward quantification of the size of the observed differences.
Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
This paper addresses these challenges in developing one of the first deep clustering methods for tabular data: Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS).
A Spectral Method for Assessing and Combining Multiple Data Visualizations
Then it leverages the eigenscores to obtain a consensus visualization, which has much improved { quality over the individual visualizations in capturing the underlying true data structure.}
Feature-Based Time-Series Analysis in R using the theft Package
With an increasing volume and complexity of time-series datasets in the sciences and industry, theft provides a standardized framework for comprehensively quantifying and interpreting informative structure in time series.
