Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Hoffswell, Jane"'
Fact-checking data claims requires data evidence retrieval and analysis, which can become tedious and intractable when done manually. This work presents Aletheia, an automated fact-checking prototype designed to facilitate data claims verification an
Externí odkaz:
http://arxiv.org/abs/2409.10713
Autor:
Wang, Huichen Will, Hoffswell, Jane, Thane, Sao Myat Thazin, Bursztyn, Victor S., Bearfield, Cindy Xiong
Large Language Models (LLMs) have been adopted for a variety of visualizations tasks, but how far are we from perceptually aware LLMs that can predict human takeaways? Graphical perception literature has shown that human chart takeaways are sensitive
Externí odkaz:
http://arxiv.org/abs/2408.06837
The ubiquity and on-the-go availability of mobile devices makes them central to many tasks such as interpersonal communication and media consumption. However, despite the potential of mobile devices for on-demand exploratory data visualization, exist
Externí odkaz:
http://arxiv.org/abs/2404.11602
While paper instructions are one of the mainstream medium for sharing knowledge, consuming such instructions and translating them into activities are inefficient due to the lack of connectivity with physical environment. We present PaperToPlace, a no
Externí odkaz:
http://arxiv.org/abs/2308.13924
Autor:
Chen, Chen, Hoffswell, Jane, Guo, Shunan, Rossi, Ryan, Chan, Yeuk-Yin, Du, Fan, Koh, Eunyee, Liu, Zhicheng
Computational notebooks such as Jupyter are popular for exploratory data analysis and insight finding. Despite the module-based structure, notebooks visually appear as a single thread of interleaved cells containing text, code, visualizations, and ta
Externí odkaz:
http://arxiv.org/abs/2308.09802
Designing responsive visualizations for various screen types can be tedious as authors must manage multiple chart~versions across design iterations. Automated approaches for responsive visualization must take into account the user's need for agency i
Externí odkaz:
http://arxiv.org/abs/2308.05136
Autor:
Narechania, Arpit, Du, Fan, Sinha, Atanu R, Rossi, Ryan A., Hoffswell, Jane, Guo, Shunan, Koh, Eunyee, Navathe, Shamkant B., Endert, Alex
Selecting relevant data subsets from large, unfamiliar datasets can be difficult. We address this challenge by modeling and visualizing two kinds of auxiliary information: (1) quality - the validity and appropriateness of data required to perform cer
Externí odkaz:
http://arxiv.org/abs/2303.01575
Autor:
Aponte, Ryan, Rossi, Ryan A., Guo, Shunan, Hoffswell, Jane, Lipka, Nedim, Xiao, Chang, Chan, Gromit, Koh, Eunyee, Ahmed, Nesreen
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives
Externí odkaz:
http://arxiv.org/abs/2212.14077
Autor:
Chen, April, Rossi, Ryan, Lipka, Nedim, Hoffswell, Jane, Chan, Gromit, Guo, Shunan, Koh, Eunyee, Kim, Sungchul, Ahmed, Nesreen K.
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without t
Externí odkaz:
http://arxiv.org/abs/2212.12040
Autor:
Chanpuriya, Sudhanshu, Rossi, Ryan A., Kim, Sungchul, Yu, Tong, Hoffswell, Jane, Lipka, Nedim, Guo, Shunan, Musco, Cameron
Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit at least one of two limitations. First, time is assumed to be discret
Externí odkaz:
http://arxiv.org/abs/2210.00032