Zobrazeno 1 - 10
of 1 119
pro vyhledávání: '"Endert, P."'
Autor:
Bertucci, Donald, Endert, Alex
Variational Autoencoders are widespread in Machine Learning, but are typically explained with dense math notation or static code examples. This paper presents VAE Explainer, an interactive Variational Autoencoder running in the browser to supplement
Externí odkaz:
http://arxiv.org/abs/2409.09011
Recently, large language models (LLMs) have shown great promise in translating natural language (NL) queries into visualizations, but their "black-box" nature often limits explainability and debuggability. In response, we present a comprehensive text
Externí odkaz:
http://arxiv.org/abs/2408.13391
We present ProvenanceWidgets, a Javascript library of UI control elements such as radio buttons, checkboxes, and dropdowns to track and dynamically overlay a user's analytic provenance. These in situ overlays not only save screen space but also minim
Externí odkaz:
http://arxiv.org/abs/2407.17431
The recent prevalence of publicly accessible, large medical imaging datasets has led to a proliferation of artificial intelligence (AI) models for cardiovascular image classification and analysis. At the same time, the potentially significant impacts
Externí odkaz:
http://arxiv.org/abs/2404.16174
Visual augmentations are commonly added to charts and graphs in order to convey richer and more nuanced information about relationships in the data. However, many design spaces proposed for categorizing augmentations were defined in a top-down manner
Externí odkaz:
http://arxiv.org/abs/2404.12952
Autor:
Guo, Grace, Kumar, Aishwarya Mudgal Sunil, Gupta, Adit, Coscia, Adam, MacLellan, Chris, Endert, Alex
Intelligent tutoring systems leverage AI models of expert learning and student knowledge to deliver personalized tutoring to students. While these intelligent tutors have demonstrated improved student learning outcomes, it is still unclear how teache
Externí odkaz:
http://arxiv.org/abs/2404.12944
Explainable AI (XAI) tools represent a turn to more human-centered and human-in-the-loop AI approaches that emphasize user needs and perspectives in machine learning model development workflows. However, while the majority of ML resources available t
Externí odkaz:
http://arxiv.org/abs/2404.02081
Autor:
Coscia, Adam, Sapers, Haley M., Deutsch, Noah, Khurana, Malika, Magyar, John S., Parra, Sergio A., Utter, Daniel R., Wipfler, Rebecca L., Caress, David W., Martin, Eric J., Paduan, Jennifer B., Hendrie, Maggie, Lombeyda, Santiago, Mushkin, Hillary, Endert, Alex, Davidoff, Scott, Orphan, Victoria J.
Scientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extre
Externí odkaz:
http://arxiv.org/abs/2403.04761
The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deployi
Externí odkaz:
http://arxiv.org/abs/2403.04760
Autor:
Coscia, Adam, Endert, Alex
Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVis, a
Externí odkaz:
http://arxiv.org/abs/2403.04758