Results 231 to 240 of about 691,007 (262)

Learning structured representations

Neurocomputing, 2003
Abstract SHRUTI is a connectionist model that demonstrates how a network of neuron-like elements can encode a large body of semantic, episodic, and causal knowledge, and rapidly make decisions and perform explanatory and predictive reasoning. To further ground this model in the functioning of the brain it must be shown that components of the model ...
Lokendra Shastri, Carter Wendelken
openaire   +1 more source

Structural Online Learning

2016
We study the problem of learning ensembles in the online setting, when the hypotheses are selected out of a base family that may be a union of possibly very complex sub-families. We prove new theoretical guarantees for the online learning of such ensembles in terms of the sequential Rademacher complexities of these sub-families.
Mehryar Mohri, Scott Yang
openaire   +1 more source

The Cost of Structure Learning

Journal of Cognitive Neuroscience, 2017
Abstract Human learning is highly efficient and flexible. A key contributor to this learning flexibility is our ability to generalize new information across contexts that we know require the same behavior and to transfer rules to new contexts we encounter.
openaire   +2 more sources

Structural learning with forgetting

Neural Networks, 1996
Abstract It is widely known that, despite its popularity, back propagation learning suffers from various difficulties. There have been many studies aiming at the solution of these. Among them there are a class of learning algorithms, which I call structural learning, aiming at small-sized networks requiring less computational cost.
openaire   +1 more source

Structure and Learning

2022
This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 ...
Dong, Tiansi, Rettinger, Achim, Tang, Jie, Tversky, Barbara, Harmelen, Frank van   +4 more
openaire   +1 more source

Learning structurally indeterminate clauses

1998
This paper describes a new kind of language bias, S-structural indeterminate clauses, which takes into account the meaning of predicates that play a key role in the complexity of learning in structural domains. Structurally indeterminate clauses capture an important background knowledge in structural domains such as medicine, chemistry or computational
Zucker, Jean-Daniel, Ganascia, Jean-Gabriel   +1 more
openaire   +2 more sources

Collaborative structure and feature learning for multi-view clustering

Information Fusion, 2023
Jindong Xu, Meiqi Gu, Weiqing Yan
exaly  

Entorhinal and ventromedial prefrontal cortices abstract and generalize the structure of reinforcement learning problems

Neuron, 2021
Alon Baram, Hamed Nili
exaly  

DeepLSS: Breaking Parameter Degeneracies in Large-Scale Structure with Deep-Learning Analysis of Combined Probes

Physical Review X, 2022
Tomasz Kacprzak, Jänis Fluri
exaly  

Improved K2 algorithm for Bayesian network structure learning

Engineering Applications of Artificial Intelligence, 2020
Hamid Beigy
exaly