Zobrazeno 1 - 10
of 291
pro vyhledávání: '"Berger, Matthew"'
Publikováno v:
2023 IEEE Visualization and Visual Analytics (VIS), Melbourne, Australia, 2023, pp. 221-225
As applications of generative AI become mainstream, it is important to understand what generative models are capable of producing, and the extent to which one can predictably control their outputs. In this paper, we propose a visualization design, na
Externí odkaz:
http://arxiv.org/abs/2406.19987
Transfer function design is crucial in volume rendering, as it directly influences the visual representation and interpretation of volumetric data. However, creating effective transfer functions that align with users' visual objectives is often chall
Externí odkaz:
http://arxiv.org/abs/2406.15634
We present CUPID: a visualization method for the contextual understanding of prompt-conditioned image distributions. CUPID targets the visual analysis of distributions produced by modern text-to-image generative models, wherein a user can specify a s
Externí odkaz:
http://arxiv.org/abs/2406.07699
In recent years, considerable work has been devoted to explaining predictive, deep learning-based models, and in turn how to evaluate explanations. An important class of evaluation methods are ones that are human-centered, which typically require the
Externí odkaz:
http://arxiv.org/abs/2406.07702
Autor:
Sahoo, Saroj, Berger, Matthew
We present a method for learning neural representations of flow maps from time-varying vector field data. The flow map is pervasive within the area of flow visualization, as it is foundational to numerous visualization techniques, e.g. integral curve
Externí odkaz:
http://arxiv.org/abs/2211.03192
Autor:
Zhao, Zhenge, Motta, Danilo, Berger, Matthew, Levine, Joshua A., Kuzucu, Ismail B., Fleischman, Robert B., Paiva, Afonso, Scheidegger, Carlos
Civil engineers use numerical simulations of a building's responses to seismic forces to understand the nature of building failures, the limitations of building codes, and how to determine the latter to prevent the former. Such simulations generate l
Externí odkaz:
http://arxiv.org/abs/2109.00197
Autor:
Conway, Charles R., Aaronson, Scott T., Sackeim, Harold A., Duffy, Walter, Stedman, Mary, Quevedo, João, Allen, Rebecca M., Riva-Posse, Patricio, Berger, Matthew A., Alva, Gustavo, Malik, Mohd Azfar, Dunner, David L., Cichowicz, Ivan, Luing, Heather, Zajecka, John, Nahas, Ziad, Mickey, Brian J., Kablinger, Anita S., Kriedt, Christopher L., Bunker, Mark T., Lee, Ying-Chieh (Lisa), Shy, Olivia, Majewski, Shannon, Olin, Bryan, Tran, Quyen, Rush, A. John
Publikováno v:
In Brain Stimulation March-April 2024 17(2):448-459
We present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value. By set
Externí odkaz:
http://arxiv.org/abs/2104.04523
Autor:
Sahoo, Saroj, Berger, Matthew
We propose a visual analytics system to help a user analyze and steer zero-shot learning models. Zero-shot learning has emerged as a viable scenario for categorizing data that consists of no labeled examples, and thus a promising approach to minimize
Externí odkaz:
http://arxiv.org/abs/2009.05254
Autor:
Berger, Matthew
In this paper we introduce a method for visually analyzing contextualized embeddings produced by deep neural network-based language models. Our approach is inspired by linguistic probes for natural language processing, where tasks are designed to pro
Externí odkaz:
http://arxiv.org/abs/2009.02554