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pro vyhledávání: '"Joshua, V"'
Autor:
Birodkar, Vighnesh, Barcik, Gabriel, Lyon, James, Ioffe, Sergey, Minnen, David, Dillon, Joshua V.
For learned image representations, basic autoencoders often produce blurry results. Reconstruction quality can be improved by incorporating additional penalties such as adversarial (GAN) and perceptual losses. Arguably, these approaches lack a princi
Externí odkaz:
http://arxiv.org/abs/2409.02529
Autor:
Streeter, Matthew, Dillon, Joshua V.
It is often useful to have polynomial upper or lower bounds on a one-dimensional function that are valid over a finite interval, called a trust region. A classical way to produce polynomial bounds of degree $k$ involves bounding the range of the $k$t
Externí odkaz:
http://arxiv.org/abs/2308.00679
Autor:
Vedadi, Elahe, Dillon, Joshua V., Mansfield, Philip Andrew, Singhal, Karan, Afkanpour, Arash, Morningstar, Warren Richard
Conventional federated learning algorithms train a single global model by leveraging all participating clients' data. However, due to heterogeneity in client generative distributions and predictive models, these approaches may not appropriately appro
Externí odkaz:
http://arxiv.org/abs/2305.13672
Autor:
Shields, Joshua V., Arunachalam, Prasiddha, Kerzendorf, Wolfgang, Hughes, John P., Biriouk, Sofia, Monk, Hayden, Buchner, Johannes
The community agrees that Type Ia supernovae arise from Carbon/Oxygen white dwarfs undergoing thermonuclear runaway. However, the full progenitor system and the process that prompts the white dwarf to explode remain unknown. Most current models sugge
Externí odkaz:
http://arxiv.org/abs/2305.03750
Autor:
Streeter, Matthew, Dillon, Joshua V.
We present a new algorithm for automatically bounding the Taylor remainder series. In the special case of a scalar function $f: \mathbb{R} \to \mathbb{R}$, our algorithm takes as input a reference point $x_0$, trust region $[a, b]$, and integer $k \g
Externí odkaz:
http://arxiv.org/abs/2212.11429
Autor:
Ruan, Yangjun, Singh, Saurabh, Morningstar, Warren, Alemi, Alexander A., Ioffe, Sergey, Fischer, Ian, Dillon, Joshua V.
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning (SSL) enable leveraging large unlabeled corpora for state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2211.09981
Publikováno v:
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021, pp. 1007-1011
Bayesian Optimization (BO) is a well-studied hyperparameter tuning technique that is more efficient than grid search for high-cost, high-parameter machine learning problems. Echocardiography is a ubiquitous modality for evaluating heart structure and
Externí odkaz:
http://arxiv.org/abs/2211.09888
Publikováno v:
Journal of Small Business and Enterprise Development, 2023, Vol. 31, Issue 1, pp. 191-225.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JSBED-10-2022-0409
Autor:
Shields, Joshua V., Kerzendorf, Wolfgang, Hosek Jr., Matthew W., Shen, Ken J., Rest, Armin, Do, Tuan, Lu, Jessica R., Fullard, Andrew G., Strampelli, Giovanni, Zenteno, Alfredo
Type Ia Supernovae (SNe Ia) are securely understood to come from the thermonuclear explosion of a white dwarf as a result of binary interaction, but the nature of that binary interaction and the secondary object is uncertain. Recently, a double white
Externí odkaz:
http://arxiv.org/abs/2206.04095