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View article: InfoClus: Informative Clustering of High-dimensional Data Embeddings
InfoClus: Informative Clustering of High-dimensional Data Embeddings Open
Developing an understanding of high-dimensional data can be facilitated by visualizing that data using dimensionality reduction. However, the low-dimensional embeddings are often difficult to interpret. To facilitate the exploration and in…
View article: Large Language Models Reflect the Ideology of their Creators
Large Language Models Reflect the Ideology of their Creators Open
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) …
View article: Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE Open
This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize preserving either local neighborhoods or global distances, b…
View article: Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization
Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization Open
Unsupervised representation learning techniques are commonly employed to analyze high-dimensional or unstructured data. In some cases, users may have prior knowledge of the topology of the data, such as a known cluster structure or the fac…
View article: Evaluating Representation Learning and Graph Layout Methods for Visualization
Evaluating Representation Learning and Graph Layout Methods for Visualization Open
Graphs and other structured data have come to the forefront in machine learning over the past few years due to the efficacy of novel representation learning methods boosting the prediction performance in various tasks. Representation learn…