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Neural Program Meta-Induction
[article]
2017
arXiv
pre-print
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Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. ...
For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance. ...
Conclusions In this work, we have contrasted two techniques for using cross-task knowledge sharing to improve neural program induction, which are referred to as adapted program induction and meta program ...
arXiv:1710.04157v1
fatcat:lawozjeljjgxnmarhguiwwcw2y
Abductive Knowledge Induction From Raw Data
[article]
2021
arXiv
pre-print
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In this paper, we present Abductive Meta-Interpretive Learning (Meta_Abd) that unites abduction and induction to learn neural networks and induce logic theories jointly from raw data. ...
Experimental results demonstrate that Meta_Abd not only outperforms the compared systems in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge ...
In this paper, we integrate neural networks with Inductive Logic Programming (ILP) [Muggleton and de Raedt, 1994 ] to enable first-order logic theory induction from raw data. ...
arXiv:2010.03514v2
fatcat:6mtsp6ucvnafnme47sama6vuru
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382)
2018
Dagstuhl Reports
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This report documents the program and the outcomes of Dagstuhl Seminar 17382 "Approaches and Applications of Inductive Programming". ...
After a short introduction to the state of the art to inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups ...
Neural Program Meta-Induction. NIPS 2017: 2077-2085 Patrice Godefroid, Hila Peleg, Rishabh Singh. Learn & Fuzz: machine learning for input fuzzing. ASE 2017: 50-59 Sahil Bhatia, Rishabh Singh. ...
doi:10.4230/dagrep.7.9.86
dblp:journals/dagstuhl-reports/SchmidMS17
fatcat:dn76mje45bhqrnevejun4qp3ku
Genetic Algorithms with DNN-Based Trainable Crossover as an Example of Partial Specialization of General Search
[chapter]
2017
Lecture Notes in Computer Science
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Universal induction relies on some general search procedure that is doomed to be inefficient. ...
We perform a feasibility study of this idea implementing such an operator in the form of a deep feedforward neural network. ...
Thus, more technically related works are the works on meta-learning in neural networks. ...
doi:10.1007/978-3-319-63703-7_10
fatcat:4phoxn6a4bgr3ekbkhwv4bsdju
Inductive logic programming at 30
[article]
2021
arXiv
pre-print
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Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. ...
We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. ...
As with the ability to learn recursive programs, the main development is to take a global view of the induction task by using meta-level search techniques. ...
arXiv:2102.10556v2
fatcat:kv7ktjbajng6jjfae3sq3ubbmu
Guest editors introduction: special issue on Inductive Logic Programming (ILP 2012)
2013
Machine Learning
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challenging applications (here, object recognition), augmenting its logical foundations (metainterpretative learning, interpretation dynamics), and combined with extra-logical machinelearning techniques (neural ...
We are grateful to the authors, the reviewers, and to the publisher Springer for giving us the opportunity to compile this reflection of current research in inductive logic programming. ...
Meta-interpretive learning is implemented with two different declarative representations: Prolog and Answer Set Programming. In the paper "Learning from Interpretation Transition" by K. Inoue, T. ...
doi:10.1007/s10994-013-5398-8
fatcat:4wi34sppyvdfdbiqlf2np32hvu
Inductive Transfer
[chapter]
2017
Encyclopedia of Machine Learning and Data Mining
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Here we describe some of them under the names of inductive transfer, transfer learning, multitask learning, meta-searching, meta-generalization, and domain adaptation. ...
In the Inductive Logic Programming (ILP) setting this is referred to as predicate invention (Stahl (1995) []). [],
Meta-Searching for Problem Solvers. ...
Hence, inductive transfer studies how to improve learning by detecting, extracting, and exploiting (meta)knowledge in the form of invariant transformations across tasks. ...
doi:10.1007/978-1-4899-7687-1_138
fatcat:ptfmvvzd45ddzeecizv3y2pspm
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
2013
Genetic Programming and Evolvable Machines
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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently ...
) different communities of meta-learning and hyper-heuristic researchers. ...
grammar-based genetic programming algorithm to evolve rule induction algorithms. ...
doi:10.1007/s10710-013-9186-9
fatcat:rxkljeappbea5m6bjmwahuqf24
Creating Rule Ensembles from Automatically-Evolved Rule Induction Algorithms
[chapter]
2010
Studies in Computational Intelligence
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First, an evolutionary algorithm (more precisely, a genetic programming algorithm) is used to automatically create complete rule induction algorithms. ...
Secondly, the automatically-evolved rule induction algorithms are used to produce rule sets that are then combined into an ensemble. ...
First, an evolutionary algorithm (a genetic programming algorithm) is used to automatically create complete rule induction algorithms. ...
doi:10.1007/978-3-642-05177-7_13
fatcat:fnbia2z6x5hzfnjuvk473lpf6u
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202)
2019
Dagstuhl Reports
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In this report the program and the outcomes of Dagstuhl Seminar 19202 "Approaches and Applications of Inductive Programming" is documented. ...
After a short introduction to the state of the art to inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups ...
Related work on this approach includes: LIME, LRP (Layerwise Relevance Propagation), www.heatmapping.org, prototype-based neural network layers: https://arxiv.org/abs/1812. 01214, neural stethoscopes: ...
doi:10.4230/dagrep.9.5.58
dblp:journals/dagstuhl-reports/RaedtEMS19
fatcat:exhpnjoh2rcjre76eqlmpmcb4q
The foundation of efficient robot learning
2020
Science
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The RL2 algorithm (11) uses DRL in the factory to learn a general small program that runs in the wild but does not necessarily have the form of a machine-learning program. ...
Inductive bias, in general, increases sample efficiency and generalizability. ...
doi:10.1126/science.aaz7597
pmid:32820109
fatcat:3sriq3u3kzd2jkuj4dgzb3jv4y
Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies
2019
JMIR Mental Health
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A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70 ...
Results A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). ...
Stacked-generalization* OR gradient-boosting-machine* OR gradient-boosted-regression-tree* OR random-forest* OR support-vector* OR Fuzzy* OR Markov* OR case-based-reasoning* OR simulated-annealing* OR inductive-logical-program ...
doi:10.2196/14108
pmid:31562756
pmcid:PMC6942187
fatcat:lphcfmt7lffcbkaxjg3h5dj2qy
Learning Compositional Rules via Neural Program Synthesis
[article]
2020
arXiv
pre-print
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Instead of directly predicting outputs from inputs, we train our model to induce the explicit system of rules governing a set of previously seen examples, drawing upon techniques from the neural program ...
Our rule-synthesis approach outperforms neural meta-learning techniques in three domains: an artificial instruction-learning domain used to evaluate human learning, the SCAN challenge datasets, and learning ...
We additionally thank Tuan Anh Le for assistance using the pyprob probabilistic programming library. M. Nye is supported by an NSF Graduate Fellowship and an MIT BCS Hilibrand Graduate Fellowship. ...
arXiv:2003.05562v2
fatcat:ksgb2kd5rvfirnis7l5i3652o4
Synaptic and Axonal Plasticity Induction in the Human Cerebral Cortex
[chapter]
2015
Innovative Medicine
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It is again compatible with plasticity induction in animals. ...
This chapter summarizes a newly developed method (quadripulse stimulation (QPS)) to induce neural plasticity in the human brain. What Is QPS? ...
In contrast, pramipexole did not affect QPS effects Neural Plasticity Induction in the Human Cerebral Cortex 302
Fig. 9 9 Protocol for the meta-plasticity experimentsFig. 10 Time courses of QPS 1.5, ...
doi:10.1007/978-4-431-55651-0_24
fatcat:7rme2ygjhvhojfhsxf2u3oel2q
Book announcements
1993
Discrete Applied Mathematics
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Chapter 13: Relations, Functions, and Induction. Rela-
tions. Functions.
Sequences.
Mathematical
induction.
Recurrence
relations.
Chapter 14: Graphs and
Trees. Graphs. ...
Chapter Guide Lines to VLSI Design of Neural Nets (IJ. Ramacher). ming and the theory of games. PART 4: NONLINEAR PROGRAMMING. Chapter 12: Nonlinear Programming. ...
doi:10.1016/0166-218x(93)90117-7
fatcat:mo4kjmd2czdg5hejcof7fmm6u4
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