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pro vyhledávání: '"Courts, Nico"'
Autor:
Coda, Elizabeth, Courts, Nico, Wight, Colby, Truong, Loc, Choi, WoongJo, Godfrey, Charles, Emerson, Tegan, Kappagantula, Keerti, Kvinge, Henry
While it is not generally reflected in the `nice' datasets used for benchmarking machine learning algorithms, the real-world is full of processes that would be best described as many-to-many. That is, a single input can potentially yield many differe
Externí odkaz:
http://arxiv.org/abs/2203.08189
Autor:
Courts, Nico, Kvinge, Henry
Many-to-one maps are ubiquitous in machine learning, from the image recognition model that assigns a multitude of distinct images to the concept of "cat" to the time series forecasting model which assigns a range of distinct time-series to a single s
Externí odkaz:
http://arxiv.org/abs/2110.06983
Autor:
Kvinge, Henry, Howland, Scott, Courts, Nico, Phillips, Lauren A., Buckheit, John, New, Zachary, Skomski, Elliott, Lee, Jung H., Tiwari, Sandeep, Hibler, Jessica, Corley, Courtney D., Hodas, Nathan O.
The field of few-shot learning has made remarkable strides in developing powerful models that can operate in the small data regime. Nearly all of these methods assume every unlabeled instance encountered will belong to a handful of known classes for
Externí odkaz:
http://arxiv.org/abs/2106.01423
Autor:
Kvinge, Henry, New, Zachary, Courts, Nico, Lee, Jung H., Phillips, Lauren A., Corley, Courtney D., Tuor, Aaron, Avila, Andrew, Hodas, Nathan O.
Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data
Externí odkaz:
http://arxiv.org/abs/2009.11253