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Statistical predicate invention
2007
Proceedings of the 24th international conference on Machine learning - ICML '07
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We propose statistical predicate invention as a key problem for statistical relational learning. ...
Since different clusterings are better for predicting different subsets of the atoms, we allow multiple cross-cutting clusterings. ...
MRC learns multiple clusterings, rather than just one, to represent the complexities in relational data. MRC is short for Multiple Relational Clusterings. ...
doi:10.1145/1273496.1273551
dblp:conf/icml/KokD07
fatcat:cq2xelz4bnbwjepzfvo6rhugi4
Analysis on the Use of Continuous Improvement, Technology and Flipped Classroom in the Teaching-Learning Process by means of Data Science
2018
Online Journal of Communication and Media Technologies
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This study proposes the creation of a virtual environment by means of the FreeDFD simulator, flipped classroom and YouTube videos with the purpose of linking the theoretical topics in the Logical of Predicates ...
This mixed research uses the Juran's methodology to achieve continuous improvement in the teaching-learning process on mathematics through technology. ...
the teaching-learning process in the Logical of Predicates Unit. ...
doi:10.12973/ojcmt/3955
fatcat:fq5fip3lzbfqvdmd7bklloknbu
Multi-task Learning for Japanese Predicate Argument Structure Analysis
[article]
2019
arXiv
pre-print
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To address this problem, we present a multi-task learning method for PASA and ENASA. ...
However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. ...
In future work, we plan to consider multiple predicates and event-nouns. ...
arXiv:1904.02244v1
fatcat:qy3scnbnpbeppfdlng3hiyiequ
Multi-Task Learning for
2019
Proceedings of the 2019 Conference of the North
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To address this problem, we present a multi-task learning method for PASA and ENASA. ...
However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. ...
Ouchi et al. (2015) jointly optimized the combinations among multiple predicates and arguments in a sentence using a bipartite graph. ...
doi:10.18653/v1/n19-1344
dblp:conf/naacl/OmoriK19
fatcat:xcpyvqmygzhytakmviyyn4nb6e
Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems
[article]
2018
arXiv
pre-print
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In this paper we extend our previous work on learning stratified answer set programs that have a single stable model to learning arbitrary (i.e., non-stratified) ones with multiple stable models. ...
To the best of our knowledge, this is the first heuristic-based ILP algorithm to induce answer set programs with multiple stable models. ...
In [16] , Sakama extends his work to learn from multiple answer sets. ...
arXiv:1802.06462v1
fatcat:iw6bn3i2zrgkzp5aniiwr53csq
Multiplicative Representations for Unsupervised Semantic Role Induction
2016
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
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In this work, we propose a neural model to learn argument embeddings from the context by explicitly incorporating dependency relations as multiplicative factors, which bias argument embeddings according ...
In unsupervised semantic role labeling, identifying the role of an argument is usually informed by its dependency relation with the predicate. ...
The embeddings are learned by predicting each argument from its context, which includes the predicate and other arguments in the same sentence. ...
doi:10.18653/v1/p16-2020
dblp:conf/acl/LuanJHL16
fatcat:qazsiwjjqfbxjhjgixjsb3xmia
Event Representations with Tensor-based Compositions
[article]
2017
arXiv
pre-print
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Our analysis shows that the tensors capture distinct usages of a predicate even when there are only subtle differences in their surface realizations. ...
The method captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. ...
Predicate Tensor Model Rather than learn a predicate-specific tensor, we instead learn two general tensors that can generate a predicatespecific tensor on the fly. ...
arXiv:1711.07611v1
fatcat:3573ytmrqvcpxo5db4mndzvhpq
Customer Credit Scoring Method Based on the SVDD Classification Model with Imbalanced Dataset
[chapter]
2010
Communications in Computer and Information Science
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The SVDD model with imbalanced dataset was analyzed and the predication method of customer credit scoring based on the SVDD model was proposed. ...
There are mainly two parts called as learning stage and predicating stage in pattern predication methods driven by dataset of samples. The processes of predication method are as following. ...
The learning and predicating accuracies are compared using the proposed method and the weighted LS-SVM model. ...
doi:10.1007/978-3-642-16397-5_4
fatcat:gdh5lua3dfd47mujpho3tfq2li
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 ...
This article argues that currently the largest gap between human and machine learning is learning algorithms' inability to perform deep transfer, that is, generalize from one domain to another domain containing ...
It is preferable to use second-order cliques as opposed to arbitrary second-order formulas because multiple different formulas over the same predicates can capture the same regularity. ...
doi:10.1609/aimag.v32i1.2330
fatcat:fe6nbklufbbmtjrvriad7w6qme
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction
[article]
2019
arXiv
pre-print
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However, most predicates only occur a handful of times making them difficult to learn. We introduce the first scene graph prediction model that supports few-shot learning of predicates. ...
We generate object representations by learning predicates trained as message passing functions within a new graph convolution framework. ...
Instead, we learn to score the predicate functions between the nodes, strengthening the correct relationships and weakening the incorrect ones over multiple iterations. ...
arXiv:1906.04876v4
fatcat:issnknt5dbcznk2jrfbuzui5va
Predicate learning in neural systems: Discovering latent generative structures
[article]
2018
arXiv
pre-print
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During the process of predicate learning, an artificial neural network exploits the naturally occurring dynamic properties of distributed computing across neuronal assemblies in order to learn predicates ...
To answer this question, we explain how a system can learn latent representational structures (i.e., predicates) from experience with wholly unstructured data. ...
Using oscillatory assembly activation to compute and to learn is potentially transformative, not only for its computational power (e.g., being able to learn from past states and learn relations over multiple ...
arXiv:1810.01127v1
fatcat:3ueslp3wqjcdjb4gajwptgbayy
Invented Predicates to Reduce Knowledge Acquisition
[chapter]
2004
Lecture Notes in Computer Science
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The aim of this study was to develop machine learning techniques that would speed up knowledge acquisition from an expert. ...
We have developed such a learning technique based on Duce's intra-construction and absorption operators [1] and applied to Ripple Down Rule (RDR) incremental knowledge acquisition [2] . ...
We ignore the possibility of constructing predicates with multiple disjunctions. • If any of new predicates are the same as existing predicates in the good or bad heaps then they are deleted (see below ...
doi:10.1007/978-3-540-30202-5_20
fatcat:rhmvncwmnvfj5ewbpkuysbsxfy
Complex Events Recognition under Uncertainty in a Sensor Network
[article]
2014
arXiv
pre-print
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MLN overcomes strong dependence on pure empirical learning by incorporating domain knowledge, in the form of user-defined rules and confidences associated with them. ...
In this work, we develop a probabilistic first order predicate logic(FOPL) based reasoning system for recognizing complex events in synchronized stream of videos, acquired from sensors with non-overlapping ...
weights learning. ...
arXiv:1411.0085v1
fatcat:hmihv4za7bfrbeeiuvsuhebznq
Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology
[chapter]
2010
Lecture Notes in Computer Science
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This provides inferences capabilities beyond current approaches: first, our system is able to handle multiple relations; second, it allows to handle dependencies between relations, by deriving new relations ...
We validate our approach by using a relational learning algorithm, which handles recursion, to learn a recursive logical theory from a text corpus on the bacterium Bacillus subtilis. ...
In the following, we will illustrate the benefit of the multiple predicate learning paradigm by outlining a typology of the learned rules. ...
doi:10.1007/978-3-642-12116-6_47
fatcat:dr7ztzsz3nfk3hgokrboied7pq
Learning the Geometric Meaning of Symbolic Abstractions for Manipulation Planning
[chapter]
2012
Lecture Notes in Computer Science
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We present an approach for learning a mapping between geometric states and logical predicates. ...
The mapping we learn in this paper achieves this translation. ...
We extend this to find geometric states for conjunctions of symbolic predicates and also-for back-tracking-to find multiple, significantly different geometric states for a predicate. ...
doi:10.1007/978-3-642-32527-4_20
fatcat:xwlsaghp4fh3zgyigni5rsbmpq
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