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Deep Transfer: A Markov Logic Approach
2011
The AI Magazine
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Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain ...
Our approach has successfully transferred learned knowledge among molecular biology, Web and social network domains. ...
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily represent-Articles SPRING 2011 3 ing the official policies, either expressed or ...
doi:10.1609/aimag.v32i1.2330
fatcat:fe6nbklufbbmtjrvriad7w6qme
Deep transfer via second-order Markov logic
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
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Humans routinely perform deep transfer, but few learning systems, if any, are capable of it. In this paper we propose an approach based on a form of second-order Markov logic. ...
Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain ...
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ARO, DARPA ...
doi:10.1145/1553374.1553402
dblp:conf/icml/DavisD09
fatcat:47bijot2g5amtajp4wsb5q3vz4
Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval
2019
Frontiers in Digital Humanities
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and (deep) learning. ...
Traditional approaches have generally treated these two aspects separately, probability and learning being the usual way to represent uncertainty in knowledge, while logical representation being the usual ...
variable discovery in statistical models and predicate invention in Inductive Logic Programming. • Deep transfer learning: Another direction of interest is deep transfer learning. ...
doi:10.3389/fdigh.2019.00006
fatcat:nsphz2frebgy5bhcybvpfcyfey
A Survey on Neural-symbolic Systems
[article]
2021
arXiv
pre-print
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In contrast, symbolic systems have exceptional cognitive intelligence through efficient reasoning, but their learning capabilities are poor. ...
In this case, an ideal intelligent system--a neural-symbolic system--with high perceptual and cognitive intelligence through powerful learning and reasoning capabilities gains a growing interest in the ...
The definition of Markov logic network is as follows [49] : Definition 2 Markov logic network is a set of pairs (F i , w i ), where F i is a formula in first-order logic, and w i represents weight of ...
arXiv:2111.08164v1
fatcat:bc33afiitnb73bmjtrfbdgkwpy
Natural Language Processing. A Machine Learning Perspective
2021
Computational Linguistics
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Part III "Deep Learning" describes the basics of deep learning modelling for classification and structured prediction tasks. The part is finalised with the basics of sequence-tosequence modelling. ...
This textbook introduces NLP from the ML standpoint elaborating on fundamental approaches and algorithms used in the field: such as statistical and deep learning models, generative and discriminative, ...
The chapter also explains such transfer learning techniques as pre-training, multi-task learning, choice of parameters for sharing, etc. ...
doi:10.1162/coli_r_00423
fatcat:rbztldsm6bgjlemmajt7wvpb6y
A Probabilistic Approach to Knowledge Translation
[article]
2015
arXiv
pre-print
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distributions, specially using Markov random fields and Markov logic networks. ...
We refer to this task as "knowledge translation" (KT). Unlike data translation and transfer learning, KT does not require any data from the source or target schema. ...
Structure Learning The structure of the target knowledge can also be learned via standard structure learning algorithms for Markov random fields or Markov logic networks. ...
arXiv:1507.03181v1
fatcat:7msy5ngi2jd4xgi4i74sljqwqu
Scalable Statistical Relational Learning for NLP
2016
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts
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Abstract: Statistical Relational Learning (SRL) is an interdisciplinary research area that combines firstorder logic and machine learning methods for probabilistic inference. ...
Prerequisites: No prior knowledge of statistical relational learning is required. ...
"Markov logic networks." ...
doi:10.18653/v1/n16-4005
dblp:conf/naacl/WangC16
fatcat:u5gngszpyvfknfk46a4q7cdjm4
STATISTICAL RELATIONAL LEARNING: A STATE-OF-THE-ART REVIEW
2019
Journal of Engineering Technology and Applied Sciences
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Taskar et al. [71] extended Markov networks (MNs) into relational Markov networks (RMNs), and Domingos and Richardson [18] into Markov logic networks (MLNs). ...
As shown in Figure 1 , SRL combines a logic-based representation with probabilistic modeling and machine learning. ...
Markov logic networks MLNs [60] is a state-of-the-art SRL model that integrates FOL representation with MN modeling. ...
doi:10.30931/jetas.594586
fatcat:qoei3pteibd6la4oqin6rvrxqi
The structure of evolved representations across different substrates for artificial intelligence
[article]
2018
arXiv
pre-print
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We show that information in Markov Brains is localized and sparsely distributed, while the other neural network substrates "smear" information about the environment across all nodes, which makes them vulnerable ...
Here we compare how recurrent artificial neural networks, long short-term memory units, and Markov Brains sense and remember their environments. ...
This material is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI-0939454. ...
arXiv:1804.01660v1
fatcat:x37kroqxzngzxnt6jp5iybgtfe
Transfer learning using computational intelligence: A survey
2015
Knowledge-Based Systems
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To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, computational intelligence has recently been applied in transfer learning ...
This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories: a) neural network-based transfer ...
Acknowledgment The work presented in this paper was supported by the Australian Research Council (ARC) under discovery grant DP140101366. ...
doi:10.1016/j.knosys.2015.01.010
fatcat:vu2ttscic5fq3nm2tkdid4fs64
Incorporation of Deep Neural Network Reinforcement Learning with Domain Knowledge
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2107.14613v1 fatcat:eczsveq7zrddbaljvvbu5twc24
and Reinforcement Learning. ...
On numerous such occasions, machine-based model development may profit essentially from the human information on the world encoded in an adequately exact structure. ...
Weight-learning in turn returns as traditional, victimizing the structure. ...
arXiv:2107.14613v2
fatcat:cddkguo3gvcgxn6s67ca26ucii
DeepPSL: End-to-end perception and reasoning with applications to zero shot learning
[article]
2021
arXiv
pre-print
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PSL represents first-order logic in terms of a convex graphical model -- Hinge Loss Markov random fields (HL-MRFs). ...
The key to our approach is to represent predicates in first-order logic using deep neural networks and then to approximately back-propagate through the HL-MRF and thus train every aspect of the first-order ...
Disclaimer: The views reflected in this article are the views of the authors and do not necessarily reflect the views of the global EY organization or its member firms. ...
arXiv:2109.13662v3
fatcat:cgz6m554izfozijciy3jpc5c6e
Structured machine learning: the next ten years
2008
Machine Learning
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The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. ...
More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. ...
Vector Logic-based Approach," Grant Reference BB/E000940/1. ...
doi:10.1007/s10994-008-5079-1
fatcat:arzjk4d7wrgffnzt4znrsfrb5q
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
[article]
2018
arXiv
pre-print
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In this work, a region-based Deep Convolutional Neural Network framework is proposed for document structure learning. ...
Exploiting the nature of region based influence modelling, a secondary level of 'intra-domain' transfer learning is used for rapid training of deep learning models for image segments. ...
Deep Convolutional Neural Network Architecure DCNNs are currently one of the most popular models for deep learning. ...
arXiv:1801.09321v3
fatcat:fd64qcf35janxkmraf7ckwt244
Markov Logic: An Interface Layer for Artificial Intelligence
2009
Synthesis Lectures on Artificial Intelligence and Machine Learning
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Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. ...
., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. ...
In this section, we describe two recent methods for deep transfer learning in relational domains using Markov logic. ...
doi:10.2200/s00206ed1v01y200907aim007
fatcat:em6ggc2ha5f4lgaie53jkdjtbu
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