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Dynamic Hypergraph Structure Learning for Multivariate Time Series Forecasting
IEEE Transactions on Big DataMultivariate time series forecasting plays an important role in many domain applications, such as air pollution forecasting and traffic forecasting. Modeling the complex dependencies among time series is a key challenging task in multivariate time series
Shun-Kai Wang +5 more
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On Learning Decision Structures
Fundamenta Informaticae, 1997A decision structure is a simple and powerful tool for organizing a decision process. It differs from a conventional decision tree in that its nodes are assigned tests that can be functions of the attributes, rather than single attributes; the branches stemming from a node can be assigned a subset of attribute values rather than a single attribute ...
Imam, Ibrahim F., Michalski, Ryszard S.
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Current Directions in Psychological Science, 2020
Social-structure learning is the process by which social groups are identified on the basis of experience. Building on models of structure learning in other domains, we formalize this problem within a Bayesian framework. According to this framework, the probabilistic assignment of individuals to groups is computed by combining information about ...
Samuel J. Gershman, Mina Cikara
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Social-structure learning is the process by which social groups are identified on the basis of experience. Building on models of structure learning in other domains, we formalize this problem within a Bayesian framework. According to this framework, the probabilistic assignment of individuals to groups is computed by combining information about ...
Samuel J. Gershman, Mina Cikara
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Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
We present Neural Structured Learning (NSL) in TensorFlow [2], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning.
Arjun Gopalan +7 more
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We present Neural Structured Learning (NSL) in TensorFlow [2], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning.
Arjun Gopalan +7 more
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Learning structurally indeterminate clauses
1998This 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 +1 more
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Learning structured representations
Neurocomputing, 2003Abstract 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
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Structuring Learning Activities
Innovations in Education and Training International, 1996SUMMARY Examination of different learning activities can give an insight into their effectiveness for the learning process. The present paper seeks to identify basic structural features of learning activities through their analysis, comparison and evaluation.
Atara Sivan, David Kember
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Learning Paradigmatic Structure
2018This chapter reviews research on the acquisition of paradigmatic structure (including research on canonical antonyms, morphological paradigms, associative inference, grammatical gender and noun classes). It discusses the second-order schema hypothesis, which views paradigmatic structure as mappings between constructions.
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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
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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
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Bi-objective evolutionary Bayesian network structure learning via skeleton constraint
Frontiers of Computer Science, 2023Ting Wu +4 more
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