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pro vyhledávání: '"Taylor, Jordan"'
Online communities are important spaces for members of marginalized groups to organize and support one another. To better understand the experiences of fat people -- a group whose marginalization often goes unrecognized -- in online communities, we c
Externí odkaz:
http://arxiv.org/abs/2410.04614
Autor:
Ajmani, Leah Hope, Foriest, Jasmine C, Taylor, Jordan, Pittman, Kyle, Gilbert, Sarah, Devito, Michael Ann
Social computing scholars have long known that people do not interact with knowledge in straightforward ways, especially in digital environments. While policies around knowledge are essential for targeting misinformation, they are value-laden; in cho
Externí odkaz:
http://arxiv.org/abs/2407.03477
Identifying the features learned by neural networks is a core challenge in mechanistic interpretability. Sparse autoencoders (SAEs), which learn a sparse, overcomplete dictionary that reconstructs a network's internal activations, have been used to i
Externí odkaz:
http://arxiv.org/abs/2405.12241
Activists, governmentsm and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers.
Externí odkaz:
http://arxiv.org/abs/2402.12505
Publikováno v:
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24)
LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work,
Externí odkaz:
http://arxiv.org/abs/2402.07864
We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as different "grid worlds", allow participants to learn two distinct key-ma
Externí odkaz:
http://arxiv.org/abs/2402.03072
Autor:
Taylor, Jordan K.
Graphical tensor notation is a simple way of denoting linear operations on tensors, originating from physics. Modern deep learning consists almost entirely of operations on or between tensors, so easily understanding tensor operations is quite import
Externí odkaz:
http://arxiv.org/abs/2402.01790
Autor:
Taylor, Jordan K., McCulloch, Ian P.
We propose a definition of wavefunction "branchings": quantum superpositions which can't be feasibly distinguished from the corresponding mixed state, even under time evolution. Our definition is largely independent of interpretations, requiring only
Externí odkaz:
http://arxiv.org/abs/2308.04494
Autor:
Wu, Tongshuang, Zhu, Haiyi, Albayrak, Maya, Axon, Alexis, Bertsch, Amanda, Deng, Wenxing, Ding, Ziqi, Guo, Bill, Gururaja, Sireesh, Kuo, Tzu-Sheng, Liang, Jenny T., Liu, Ryan, Mandal, Ihita, Milbauer, Jeremiah, Ni, Xiaolin, Padmanabhan, Namrata, Ramkumar, Subhashini, Sudjianto, Alexis, Taylor, Jordan, Tseng, Ying-Jui, Vaidos, Patricia, Wu, Zhijin, Wu, Wei, Yang, Chenyang
LLMs have shown promise in replicating human-like behavior in crowdsourcing tasks that were previously thought to be exclusive to human abilities. However, current efforts focus mainly on simple atomic tasks. We explore whether LLMs can replicate mor
Externí odkaz:
http://arxiv.org/abs/2307.10168