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pro vyhledávání: '"TANG, ALEX"'
This paper looks at a common law legal system as a learning algorithm, models specific features of legal proceedings, and asks whether this system learns efficiently. A particular feature of our model is explicitly viewing various aspects of court pr
Externí odkaz:
http://arxiv.org/abs/2209.02866
We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function under Gaussian distributions. Unlike prior work that studies the setting of zero bias, we consider the more challenging scenario whe
Externí odkaz:
http://arxiv.org/abs/2208.02711
Autor:
Jankowski, Jaclyn M., Menken, Luke G., Romanelli, Filippo, Hong, Ian S., Tang, Alex, Yoon, Richard S., Liporace, Frank A.
Publikováno v:
In Arthroplasty Today June 2024 27
We present polynomial time and sample efficient algorithms for learning an unknown depth-2 feedforward neural network with general ReLU activations, under mild non-degeneracy assumptions. In particular, we consider learning an unknown network of the
Externí odkaz:
http://arxiv.org/abs/2107.10209
Autor:
Das, Piali, Perrone, Valerio, Ivkin, Nikita, Bansal, Tanya, Karnin, Zohar, Shen, Huibin, Shcherbatyi, Iaroslav, Elor, Yotam, Wu, Wilton, Zolic, Aida, Lienart, Thibaut, Tang, Alex, Ahmed, Amr, Faddoul, Jean Baptiste, Jenatton, Rodolphe, Winkelmolen, Fela, Gautier, Philip, Dirac, Leo, Perunicic, Andre, Miladinovic, Miroslav, Zappella, Giovanni, Archambeau, Cédric, Seeger, Matthias, Dutt, Bhaskar, Rouesnel, Laurence
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline. Although these systems perform well on many datas
Externí odkaz:
http://arxiv.org/abs/2012.08483
Autor:
Vijayakumar, Gayathri, Tang, Alex, Vance, Dylan, Yoon, Richard S., Sweeney, Kyle, Blank, Alan T.
Publikováno v:
In Arthroplasty Today February 2024 25