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检索条件"任意字段=10th International Conference on Inductive Logic Programming, ILP 2000"

共 59 条记录，以下是1-10 订阅

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Online Learning of logic Based Neural Network Structures 1

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30th international conference on inductive logic programming (ilp) held as part of the 1st international Joint conference on Learning and Reasoning (IJCLR)

作者： Guimaraes, Victor Costa, Vitor Santos Univ Porto CRACS Porto Portugal Univ Porto DCC FCUP Porto Portugal

ISBN: (数字)9783030974541

ISBN: (纸本)9783030974541;9783030974534

In this paper, we present two online structure learning algorithms for NeuralLog, NeuralLog+OSLR and NeuralLog+OMIL. NeuralLog is a system that compiles first-order logic programs into neural networks. Both learning algorithms are based on Online Structure Learner by Revision (OSLR). NeuralLog+OSLR is a port of OSLR to use NeuralLog as inference engine;while NeuralLog+OMIL uses the underlying mechanism from OSLR, but with a revision operator based on Meta-Interpretive Learning. We compared both systems with OSLR and RDN-Boost on link prediction in three different datasets: Cora, UMLS and UWCSE. Our experiments showed that NeuralLog+OMIL outperforms both the compared systems on three of the four target relations from the Cora dataset and in the UMLS dataset, while both NeuralLog+OSLR and NeuralLog+OMIL outperform OSLR and RDNBoost on the UWCSE, assuming a good initial theory is provided.

关键词： Online Learning inductive logic programming Meta-Interpretive Learning Neural Network Neural-Symbolic Learning and Reasoning theory Revision from Examples

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28th international conference on inductive logic programming, ilp 2018

28th International Conference on Inductive Logic Programming...

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28th international conference on inductive logic programming, ilp 2018

ISBN: (纸本)9783319999593

the proceedings contain 10 papers. the special focus in this conference is on . the topics include: Large-Scale Assessment of Deep Relational Machines;how Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies?;diagnostics of Trains with Semantic Diagnostics Rules;the game of bridge: A challenge for ilp;Sampling-Based SAT/ASP Multi-model Optimization as a Framework for Probabilistic Inference;Explaining Black-Box Classifiers with ilp – Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules;learning Dynamics with Synchronous, Asynchronous and General Semantics;was the Year 2000 a Leap Year? Step-Wise Narrowing theories with Metagol.

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CONNER: A Concurrent ilp Learner in Description logic 1

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29th international conference on inductive logic programming

作者： Algahtani, Eyad Kazakov, Dimitar Univ York York YO10 5GH N Yorkshire England

ISBN: (数字)9783030492106

ISBN: (纸本)9783030492090;9783030492106

Machine Learning (ML) approaches can achieve impressive results, but many lack transparency or have difficulties handling data of high structural complexity. the class of ML known as inductive logic programming (ilp) draws on the expressivity and rigour of subsets of First Order logic to represent both data and models. When Description logics (DL) are used, the approach can be applied directly to knowledge represented as ontologies. ilp output is a prime candidate for explainable artificial intelligence;the expense being computational complexity. We have recently demonstrated how a critical component of ilp learners in DL, namely, cover set testing, can be speeded up through the use of concurrent processing. Here we describe the first prototype of an ilp learner in DL that benefits from this use of concurrency. the result is a fast, scalable tool that can be applied directly to large ontologies.

关键词： inductive logic programming Description logics Ontologies Parallel computing GPGPU

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LEARN TO EXPLAIN EFFICIENTLY VIA NEURAL logic inductive LEARNING 8

LEARN TO EXPLAIN EFFICIENTLY VIA NEURAL LOGIC INDUCTIVE LEAR...

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8th international conference on Learning Representations, ICLR 2020

作者： Yang, Yuan Song, Le Georgia Institute of Technology United States

the capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain problem in the scope of inductive logic programming (ilp). We propose Neural logic inductive Learning (NLIL), an efficient differentiable ilp framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art methods, we find NLIL can search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities. © 2020 8th international conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Large dataset

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26th international conference on inductive logic programming, ilp 2016

26th International Conference on Inductive Logic Programming...

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26th international conference on inductive logic programming, ilp 2016

ISBN: (纸本)9783319633411

the proceedings contain 10 papers. the special focus in this conference is on inductive logic programming. the topics include: Estimation-based search space traversal in Pilp environments;inductive logic programming meets relational databases;online structure learning for traffic management;learning through advice-seeking via transfer;distributional learning of regular formal graph system of bounded degree;learning relational dependency networks for relation extraction;towards nonmonotonic relational learning from knowledge graphs;learning predictive categories using lifted relational neural networks and generation of near-optimal solutions using ilp-guided sampling.

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Analysis and prediction of heart diseases using inductive logic and image segmentation 4th

Analysis and prediction of heart diseases using inductive lo...

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4th international conference on Data Mining and Big Data, DMBD 2019

作者： Anand Hareendran, S. Vinod Chandra, S.S. Prasad, Sreedevi Muthoot Institute of Technology and Science Kochi India University of Kerala Trivandrum India RIET Trivandrum India

ISBN: (纸本)9789813295629

the human heart's electrical activity produces currents that radiate through the surrounding tissue to the skin. When electrodes are attached to the skin, they sense these electrical currents and transmit them to an electrocardiograph monitor. the currents are then transformed into waveforms that represent the heart's depolarisation - repolarisation cycle. An electro cardiogram (ECG) shows the precise sequence of electrical events occurring in the cardiac cells throughout the process. It can be used to monitor phases of myocardial contraction and to identify rhythm and conduction disturbances. In this work an automated ECG analysis and prediction of the heart conditions are carried out using a continuous image segmentation phase using inductive logic programming (ilp) system. ECG taken from the patients is fed to the system as input and it uses the background knowledge to predict the possible underlying heart disorders. A continuous image segmentation module runs through out the process which identifies the leads and graph distortions by eliminating the outliers. the extracted lead graphs are compared with the in store ilp system database and possible predictions are made. the system performance was automatically compared (by Brier score method) with the MIT-BIH ECG database (PhysioNet) and a prediction accuracy of 97.8% was obtained in lead-lead prediction. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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Was the Year 2000 a Leap Year? Step-Wise Narrowing theories with Metagol 1

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28th international conference on inductive logic programming (ilp)

作者： Siebers, Michael Schmid, Ute Univ Bamberg Cognit Syst Bamberg Germany

ISBN: (数字)9783319999609

ISBN: (纸本)9783319999609;9783319999593

Many people believe that every fourth year is a leap year. However, this rule is too general: year X is a leap year if X is divisible by 4 except if X is divisible by 100 except if X is divisible by 400. We call such a theory with alternating generalisation and specialisation a step-wise narrowed theory. We present and evaluate an extension to the ilp system Metagol which facilitates learning such theories. We enabled Metagol to learn over-general theories by allowing a limited number of false positives during learning. this variant is iteratively applied on a learning task. For each iteration after the first, positive examples are the false positives from the previous iteration and negative examples are the true positives from the previous iteration. Iteration continues until no more false positives are present. then, the theories are combined to a single step-wise narrowed theory. We evaluate the usefulness of our approach in the leap year domain. We can show that our approach finds solutions with fewer clauses, higher accuracy, and in shorter time.

关键词： Step-wise narrowed theory Metagol Over-generalization

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GPU-accelerated hypothesis cover set testing for learning in logic 28

GPU-accelerated hypothesis cover set testing for learning in...

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Up-and-Coming and Short Papers of the 28th international conference on inductive logic programming, UCS-ilp 2018

作者： Algahtani, Eyad Kazakov, Dimitar University of York Heslington YorkYO10 5GH United Kingdom

ilp learners are commonly implemented to consider sequentially each training example for each of the hypotheses tested. Computing the cover set of a hypothesis in this way is costly, and introduces a major bottleneck in the learning process. this computation can be implemented more efficiently through the use of data level parallelism. Here we propose a GPU-accelerated approach to this task for propositional logic and for a subset of first order logic. this approach can be used with one’s strategy of choice for the exploration of the hypothesis space. At present, the hypothesis language is limited to logic formulae using unary and binary predicates, such as those covered by certain types of description logic. the approach is tested on a commodity GPU and datasets of up to 200 million training examples, achieving run times of below 30ms per cover set computation. © 2018 CEUR-WS. All rights reserved.

关键词： Graphics processing unit

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the Most Probable Explanation for Probabilistic logic Programs with Annotated Disjunctions 1

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24th international conference on inductive logic programming (ilp)

作者： Shterionov, Dimitar Renkens, Joris Vlasselaer, Jonas Kimmig, Angelika Meert, Wannes Janssens, Gerda KULeuven Leuven Belgium

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. this encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed (general) approach.

关键词： Probabilistic logic programming Statistical relational learning Most Probable Explanation logic programs with annotated disjunctions ProbLog

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Function-Level Processor (FLP): Raising Efficiency by Operating at Function Granularity for Market-Oriented MPSoC

Function-Level Processor (FLP): Raising Efficiency by Operat...

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IEEE 25th international conference on Application-Specific Systems, Architectures and Processors (ASAP)

作者： Tabkhi, Hamed Bushey, Robert Schirner, Gunar Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Analog Devices Inc ADI Embedded Syst Prod & Technol Norwood MA USA

ISBN: (纸本)9781479936090

the exponential growth in computation demand drives chip vendors to heterogeneous architectures combining Instruction-Level Processors (ilps) and custom HW Accelerators (HWACCs) in an attempt to provide the needed processing capabilities while meeting power/energy requirements. ilps, on one hand, are highly flexible, but power inefficient. Custom HWACCs, on the other hand, are inflexible (focusing on dedicated kernels), but highly power efficient. Since, designing HWACCs for every application is cost prohibitive, large portions of applications still run inefficiently on ilps. New processing architectures are needed that combine the power efficiency of HWACCs while still retaining sufficient flexibility to realize applications across targeted market segments. this paper introduces Function-Level Processors (FLPs) to fill the gap between ilps and dedicated HWACCs. FLPs are comprised of configurable Function Blocks (FBs) implementing selected functions which are then interconnected via programmable point-to-point connections constructing an extensible/configurable macro data-path. An FLP raises programming abstraction to a Function-Set Architecture (FSA) controlling FBs allocation, configuration and scheduling. We demonstrate FLP benefits with an industry example of the Pipeline-Vision Processor (PVP). We highlight the gained flexibility by mapping 10 embedded vision applications entirely to the FLP-PVP offering up to 22.4 GOPs/s with average power of 120 mW. the results also demonstrate that our FLP-PVP solution consumes 14x-18x less power than an ilp and 5x less power than a hybrid ilp+ HWACCs solution.

关键词： fault-location problem PROCESSOR inductive logic programming dynamic efficiency Exponential Growth average power ARCHITECTURE Power (Psychology) Energy sources macro data Target marketing Connection points

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