Data Visualization
78 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Data Visualization
MIMI dataset
PJM(AEP)
Most implemented papers
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data.
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.
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.
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.
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.
VizML: A Machine Learning Approach to Visualization Recommendation
Data visualization should be accessible for all analysts with data, not just the few with technical expertise.
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks.
Addressing the Mystery of Population Decline of the Rose-Crested Blue Pipit in a Nature Preserve using Data Visualization
Two main methods for exploring patterns in data are data visualization and machine learning.
