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Statistical predicate invention

Stanley Kok, Pedro Domingos
2007 Proceedings of the 24th international conference on Machine learning - ICML '07  
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

Ricardo-Adán Salas-Rueda
2018 Online Journal of Communication and Media Technologies  
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]

Hikaru Omori, Mamoru Komachi
2019 arXiv   pre-print
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

Hikaru Omori, Mamoru Komachi
2019 Proceedings of the 2019 Conference of the North  
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]

Farhad Shakerin, Gopal Gupta
2018 arXiv   pre-print
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

Yi Luan, Yangfeng Ji, Hannaneh Hajishirzi, Boyang Li
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
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]

Noah Weber, Niranjan Balasubramanian, Nathanael Chambers
2017 arXiv   pre-print
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]

Bo Tian, Lin Nan, Qin Zheng, Lei Yang
2010 Communications in Computer and Information Science  
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

Jesse Davis, Pedro Domingos
2011 The AI Magazine  
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]

Apoorva Dornadula, Austin Narcomey, Ranjay Krishna, Michael Bernstein, Li Fei-Fei
2019 arXiv   pre-print
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]

Andrea E. Martin, Leonidas A. A. Doumas
2018 arXiv   pre-print
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]

Hendra Suryanto, Paul Compton
2004 Lecture Notes in Computer Science  
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]

Atul Kanaujia, Tae Eun Choe, Hongli Deng
2014 arXiv   pre-print
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]

Alain-Pierre Manine, Erick Alphonse, Philippe Bessières
2010 Lecture Notes in Computer Science  
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]

Chris Burbridge, Richard Dearden
2012 Lecture Notes in Computer Science  
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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