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pro vyhledávání: '"A. Howe"'
We find that the cross-entropy loss curves of neural language models empirically adhere to a scaling law with learning rate (LR) annealing over training steps ($s$): $$L(s) = L_0 + A\cdot S_1^{-\alpha} - C\cdot S_2$$ Where $S_1$ is forward area and $
Externí odkaz:
http://arxiv.org/abs/2408.11029
This research introduces the Multilevel Embedding Association Test (ML-EAT), a method designed for interpretable and transparent measurement of intrinsic bias in language technologies. The ML-EAT addresses issues of ambiguity and difficulty in interp
Externí odkaz:
http://arxiv.org/abs/2408.01966
Popular and news media often portray teenagers with sensationalism, as both a risk to society and at risk from society. As AI begins to absorb some of the epistemic functions of traditional media, we study how teenagers in two countries speaking two
Externí odkaz:
http://arxiv.org/abs/2408.01961
Multimodal AI models capable of associating images and text hold promise for numerous domains, ranging from automated image captioning to accessibility applications for blind and low-vision users. However, uncertainty about bias has in some cases lim
Externí odkaz:
http://arxiv.org/abs/2408.01959
Equitable urban transportation applications require high-fidelity digital representations of the built environment: not just streets and sidewalks, but bike lanes, marked and unmarked crossings, curb ramps and cuts, obstructions, traffic signals, sig
Externí odkaz:
http://arxiv.org/abs/2408.00932
Autor:
Wen, Bingbing, Yao, Jihan, Feng, Shangbin, Xu, Chenjun, Tsvetkov, Yulia, Howe, Bill, Wang, Lucy Lu
Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in LLM systems. In this survey, we introduce a framework to examine abstention fr
Externí odkaz:
http://arxiv.org/abs/2407.18418
Autor:
Howe, Nikolaus, Zajac, Michał, McKenzie, Ian, Hollinsworth, Oskar, Tseng, Tom, Bacon, Pierre-Luc, Gleave, Adam
Language model capabilities predictably improve from scaling a model's size and training data. Motivated by this, increasingly large language models have been trained, yielding an array of impressive capabilities. Yet these models are vulnerable to a
Externí odkaz:
http://arxiv.org/abs/2407.18213
Applications to support pedestrian mobility in urban areas require a complete, and routable graph representation of the built environment. Globally available information, including aerial imagery provides a scalable source for constructing these path
Externí odkaz:
http://arxiv.org/abs/2407.16875
Autor:
Astles, Thomas, McHugh, James G., Zhang, Rui, Guo, Qian, Howe, Madeleine, Wu, Zefei, Indykiewicz, Kornelia, Summerfield, Alex, Goodwin, Zachary A. H., Slizovskiy, Sergey, Domaretskiy, Daniil, Geim, Andre K., Falko, Vladimir, Grigorieva, Irina V.
Publikováno v:
Nature Communications 15, 6933 (2024)
The ongoing efforts to optimize Li-ion batteries led to the interest in intercalation of nanoscale layered compounds, including bilayer graphene. Its lithium intercalation has been demonstrated recently but the mechanisms underpinning the storage cap
Externí odkaz:
http://arxiv.org/abs/2407.07838
Tendon-driven robotic catheters are capable of precise execution of minimally invasive cardiac procedures including ablations and imaging. These procedures require accurate mathematical models of not only the catheter and tendons but also their inter
Externí odkaz:
http://arxiv.org/abs/2407.07618