Zobrazeno 1 - 10
of 14 275
pro vyhledávání: '"Frank Michael"'
Expert systems are effective tools for automatically identifying energy efficiency potentials in manufacturing, thereby contributing significantly to global climate targets. These systems analyze energy data, pinpoint inefficiencies, and recommend op
Externí odkaz:
http://arxiv.org/abs/2411.01272
Autor:
Röder, Manuel, Schleif, Frank-Michael
This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients t
Externí odkaz:
http://arxiv.org/abs/2409.12575
Autor:
Röder, Manuel, Schleif, Frank-Michael
We present a numerically robust, computationally efficient approach for non-I.I.D. data stream sampling in federated client systems, where resources are limited and labeled data for local model adaptation is sparse and expensive. The proposed method
Externí odkaz:
http://arxiv.org/abs/2408.17108
While high-performing language models are typically trained on hundreds of billions of words, human children become fluent language users with a much smaller amount of data. What are the features of the data they receive, and how do these features su
Externí odkaz:
http://arxiv.org/abs/2408.03617
Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electr
Externí odkaz:
http://arxiv.org/abs/2407.04377
Autor:
Long, Bria, Xiang, Violet, Stojanov, Stefan, Sparks, Robert Z., Yin, Zi, Keene, Grace E., Tan, Alvin W. M., Feng, Steven Y., Zhuang, Chengxu, Marchman, Virginia A., Yamins, Daniel L. K., Frank, Michael C.
Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''data gap'' is a key challenge both for building intelligent artificial
Externí odkaz:
http://arxiv.org/abs/2406.10447
Autor:
Tan, Alvin Wei Ming, Yu, Sunny, Long, Bria, Ma, Wanjing Anya, Murray, Tonya, Silverman, Rebecca D., Yeatman, Jason D., Frank, Michael C.
How (dis)similar are the learning trajectories of vision-language models and children? Recent modeling work has attempted to understand the gap between models' and humans' data efficiency by constructing models trained on less data, especially multim
Externí odkaz:
http://arxiv.org/abs/2406.10215
Autor:
Hu, Jennifer, Frank, Michael C.
Developmental psychologists have argued about when cognitive capacities such as language understanding or theory of mind emerge. These debates often hinge on the concept of "task demands" -- the auxiliary challenges associated with performing a parti
Externí odkaz:
http://arxiv.org/abs/2404.02418
Autor:
Spáčilová, Libuše
Publikováno v:
Listy filologické / Folia philologica, 2019 Jan 01. 142(1/2), 229-232.
Externí odkaz:
https://www.jstor.org/stable/26779378
