Zobrazeno 1 - 10
of 18 517
pro vyhledávání: '"Tillman A"'
Autor:
Tillman, Megan Taylor, Burkhart, Blakesley, Tonnesen, Stephanie, Bird, Simeon, Bryan, Greg L.
We study the effects of AGN feedback on the Lyman-$\alpha$ forest 1D flux power spectrum (P1D). Using the Simba cosmological-hydrodynamic simulations, we examine the impact that adding different AGN feedback modes has on the predicted P1D. We find th
Externí odkaz:
http://arxiv.org/abs/2410.05383
Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedding system OWL2Vec*. While OWL
Externí odkaz:
http://arxiv.org/abs/2408.06310
Ensuring the reliability and safety of automated decision-making is crucial. It is well-known that data distribution shifts in machine learning can produce unreliable outcomes. This paper proposes a new approach for measuring the reliability of predi
Externí odkaz:
http://arxiv.org/abs/2407.07821
Autor:
Sikar, Daniel, Garcez, Artur, Bloomfield, Robin, Weyde, Tillman, Peeroo, Kaleem, Singh, Naman, Hutchinson, Maeve, Laksono, Dany, Reljan-Delaney, Mirela
This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM is obtained by leveraging softmax outputs and clustering techniques t
Externí odkaz:
http://arxiv.org/abs/2407.07818
Autor:
Zhang, Yuxuan, Sazzad, T. M., Song, Yangyang, Chang, Spencer J., Chowdhry, Ritesh, Mejia, Tomas, Hampton, Anna, Kucharski, Shelby, Gerber, Stefan, Tillman, Barry, Resende, Marcio F. R., Hammond, William M., Wilson, Chris H., Zare, Alina, Koppal, Sanjeev J.
Hyper-spectral imaging has recently gained increasing attention for use in different applications, including agricultural investigation, ground tracking, remote sensing and many other. However, the high cost, large physical size and complicated opera
Externí odkaz:
http://arxiv.org/abs/2406.19560
Autor:
Gao, Leo, la Tour, Tom Dupré, Tillman, Henk, Goh, Gabriel, Troll, Rajan, Radford, Alec, Sutskever, Ilya, Leike, Jan, Wu, Jeffrey
Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models learn many concepts, autoencoders need to be
Externí odkaz:
http://arxiv.org/abs/2406.04093
In this work, we introduce a novel approach to regularization in multivariable regression problems. Our regularizer, called DLoss, penalises differences between the model's derivatives and derivatives of the data generating function as estimated from
Externí odkaz:
http://arxiv.org/abs/2405.00555
Quantum repeaters are necessary to fully realize the capabilities of the emerging quantum internet, especially applications involving distributing entanglement across long distances. A more general notion of this can be called a quantum switch, which
Externí odkaz:
http://arxiv.org/abs/2404.18818
Autor:
Han, Jun, Chen, Zixiang, Li, Yongqian, Kou, Yiwen, Halperin, Eran, Tillman, Robert E., Gu, Quanquan
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e.g., disease progression prediction, clinical trial design, and health economics and outcomes research. Despite wide usability,
Externí odkaz:
http://arxiv.org/abs/2404.12314
Publikováno v:
E3S Web of Conferences, Vol 349, p 06004 (2022)
Recent years have seen much interest in business models as vehicles towards sustainability, cf. [1]. Conventional LCA, however, fails to properly capture the environmental impacts of a business model. Here, we introduce the background and the princip
Externí odkaz:
https://doaj.org/article/f4d1dae2d9804ad5bc9ae018f34ee2bd
