Zobrazeno 1 - 10
of 3 255
pro vyhledávání: '"Marks Robert"'
Autor:
Ruparell, Karan, Marks, Robert J., Wood, Andy, Hunt, Kieran M. R., Cloke, Hannah L., Prudhomme, Christel, Pappenberger, Florian, Chantry, Matthew
Long Short Term Memory networks (LSTMs) are used to build single models that predict river discharge across many catchments. These models offer greater accuracy than models trained on each catchment independently if using the same data. However, the
Externí odkaz:
http://arxiv.org/abs/2410.16343
Biased datasets are ubiquitous and present a challenge for machine learning. For a number of categories on a dataset that are equally important but some are sparse and others are common, the learning algorithms will favor the ones with more presence.
Externí odkaz:
http://arxiv.org/abs/2312.15307
The outcome of all time series cannot be forecast, e.g. the flipping of a fair coin. Others, like the repeated {01} sequence {010101...} can be forecast exactly. Algorithmic information theory can provide a measure of forecastability that lies betwee
Externí odkaz:
http://arxiv.org/abs/2304.10752
Autor:
Marks, Robert B.
Publikováno v:
Southern California Quarterly, 2023 Jul 01. 105(2), 105-141.
Externí odkaz:
https://www.jstor.org/stable/27280235
Autor:
Babu, Punuri Jayasekhar, Tirkey, Akriti, Paul, Abraham Abbey, Kristollari, Kathelina, Barman, Jugal, Panda, Kingshuk, Sinha, Neha, Babu, Birudu Ravi, Marks, Robert S.
Publikováno v:
In Engineered Regeneration September 2024 5(3):326-341
The identification of out-of-distribution content is critical to the successful implementation of neural networks. Watchdog techniques have been developed to support the detection of these inputs, but the performance can be limited by the amount of a
Externí odkaz:
http://arxiv.org/abs/2108.09375