Data Visualization
80 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Data Visualization
MIMI dataset
PJM(AEP)
Latest papers
Supervised Stochastic Neighbor Embedding Using Contrastive Learning
Stochastic neighbor embedding (SNE) methods $t$-SNE, UMAP are two most popular dimensionality reduction methods for data visualization.
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems.
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
It focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets.
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
collection-space-navigator/csn
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We introduce the Collection Space Navigator (CSN), a browser-based visualization tool to explore, research, and curate large collections of visual digital artifacts that are associated with multidimensional data, such as vector embeddings or tables of metadata.
Context-Aware Chart Element Detection
As a prerequisite of chart data extraction, the accurate detection of chart basic elements is essential and mandatory.
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.
