Zobrazeno 1 - 10
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pro vyhledávání: '"A. Donald"'
Segmented light field images can serve as a powerful representation in many of computer vision tasks exploiting geometry and appearance of objects, such as object pose tracking. In the light field domain, segmentation presents an additional objective
Externí odkaz:
http://arxiv.org/abs/2411.13840
Modern cryptography, such as Rivest Shamir Adleman (RSA) and Secure Hash Algorithm (SHA), has been designed by humans based on our understanding of cryptographic methods. Neural Network (NN) based cryptography is being investigated due to its ability
Externí odkaz:
http://arxiv.org/abs/2411.10287
Autor:
Mohammed, Abdurahman Ali, Fonder, Catherine, Sakaguchi, Donald S., Tavanapong, Wallapak, Mallapragada, Surya K., Idris, Azeez
We present a new annotated microscopic cellular image dataset to improve the effectiveness of machine learning methods for cellular image analysis. Cell counting is an important step in cell analysis. Typically, domain experts manually count cells in
Externí odkaz:
http://arxiv.org/abs/2411.08992
Autor:
Debski, Maya H., Zeimann, Gregory R., Hill, Gary J., Schneider, Donald P., Morabito, Leah, Dalton, Gavin, Jarvis, Matt J., Cooper, Erin Mentuch, Ciardullo, Robin, Gawiser, Eric, Jurlin, Nika
We combine the power of blind integral field spectroscopy from the Hobby-Eberly Telescope (HET) Dark Energy Experiment (HETDEX) with sources detected by the Low Frequency Array (LOFAR) to construct the HETDEX-LOFAR Spectroscopic Redshift Catalog. Sta
Externí odkaz:
http://arxiv.org/abs/2411.08974
Autor:
Chaika, Jon, Robertson, Donald
We show that there is a rank 1 transformation that is mildly mixing but does not have minimal self-joinings, answering a question of Thouvenot.
Comment: 46 pages, 6 figures
Comment: 46 pages, 6 figures
Externí odkaz:
http://arxiv.org/abs/2411.08180
Autor:
Prabhune, Sonal, Berndt, Donald J.
Knowing that the generative capabilities of large language models (LLM) are sometimes hampered by tendencies to hallucinate or create non-factual responses, researchers have increasingly focused on methods to ground generated outputs in factual data.
Externí odkaz:
http://arxiv.org/abs/2411.11895
Human understanding of language is robust to different word choices as far as they represent similar semantic concepts. To what extent does our human intuition transfer to language models, which represent all subwords as distinct embeddings? In this
Externí odkaz:
http://arxiv.org/abs/2411.04530
Self-supervised learning (SSL) has grown in interest within the speech processing community, since it produces representations that are useful for many downstream tasks. SSL uses global and contextual methods to produce robust representations, where
Externí odkaz:
http://arxiv.org/abs/2411.04379
Autor:
Andrews, Donald W. K., Li, Ming
This paper considers nonparametric estimation and inference in first-order autoregressive (AR(1)) models with deterministically time-varying parameters. A key feature of the proposed approach is to allow for time-varying stationarity in some time per
Externí odkaz:
http://arxiv.org/abs/2411.00358
Autor:
Mikhaeil, Jonas M., Green, Donald P.
Building on statistical foundations laid by Neyman [1923] a century ago, a growing literature focuses on problems of causal inference that arise in the context of randomized experiments where the target of inference is the average treatment effect in
Externí odkaz:
http://arxiv.org/abs/2411.00191