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pro vyhledávání: '"Exact learning"'
Causal learning from data has received much attention in recent years. One way of capturing causal relationships is by utilizing Bayesian networks. There, one recovers a weighted directed acyclic graph, in which random variables are represented by ve
Externí odkaz:
http://arxiv.org/abs/2410.16100
Autor:
Dominé, Clémentine C. J., Anguita, Nicolas, Proca, Alexandra M., Braun, Lukas, Kunin, Daniel, Mediano, Pedro A. M., Saxe, Andrew M.
Biological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models relies on their ability to build task specific representation, a process i
Externí odkaz:
http://arxiv.org/abs/2409.14623
There has been a growing interest in causal learning in recent years. Commonly used representations of causal structures, including Bayesian networks and structural equation models (SEM), take the form of directed acyclic graphs (DAGs). We provide a
Externí odkaz:
http://arxiv.org/abs/2406.15229
Autor:
Ngoc, Viet Pham, Wiklicky, Herbert
In this paper, we study the tunable quantum neural network architecture in the quantum exact learning framework with access to a uniform quantum example oracle. We present an approach that uses amplitude amplification to correctly tune the network to
Externí odkaz:
http://arxiv.org/abs/2309.00561
Akademický článek
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Autor:
Afshar, Ramtin, Goodrich, Michael T.
Given a directed graph, G=(V,E), a path query, path(u,v), returns whether there is a directed path from u to v in G, for u,v vertices in V. Given only V, exactly learning all the edges in G using path queries is often impossible, since path queries c
Externí odkaz:
http://arxiv.org/abs/2208.04216
Autor:
Moshkov, Mikhail
In this paper, based on results of exact learning and test theory, we study arbitrary infinite binary information systems each of which consists of an infinite set of elements and an infinite set of two-valued functions (attributes) defined on the se
Externí odkaz:
http://arxiv.org/abs/2201.04506
We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite. For the dialect DL-LiteH, we show that ELIQs have a frontier (set of least general generalizations) that is of polynomial size and can be computed
Externí odkaz:
http://arxiv.org/abs/2204.14172
Autor:
Moshkov, Mikhail
In this paper, based on results of exact learning, test theory, and rough set theory, we study arbitrary infinite families of concepts each of which consists of an infinite set of elements and an infinite set of subsets of this set called concepts. W
Externí odkaz:
http://arxiv.org/abs/2201.08225
Autor:
Sugahara, Shouta, Ueno, Maomi
Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the margi
Externí odkaz:
http://arxiv.org/abs/2107.03018