| Nome |
# |
|
Distributed Parameter Learning for Probabilistic Ontologies, file e309ade0-d159-3969-e053-3a05fe0a2c94
|
189
|
|
Probabilistic Hybrid Knowledge Bases under the Distribution Semantics, file e309ade0-d973-3969-e053-3a05fe0a2c94
|
179
|
|
Abductive Logic Programming for Normative Reasoning and Ontologies, file e309ade0-e264-3969-e053-3a05fe0a2c94
|
151
|
|
Tableau reasoning for description logics and its extension to probabilities, file e309ade1-4985-3969-e053-3a05fe0a2c94
|
150
|
|
Probabilistic logic programming on the web, file e309ade0-e445-3969-e053-3a05fe0a2c94
|
148
|
|
Probabilistic Logical Inference On the Web, file e309ade0-d1ba-3969-e053-3a05fe0a2c94
|
142
|
|
A web system for reasoning with probabilistic OWL, file e309ade0-d9a1-3969-e053-3a05fe0a2c94
|
139
|
|
Expectation Maximization in Deep Probabilistic Logic Programming, file e309ade2-9cf4-3969-e053-3a05fe0a2c94
|
138
|
|
Probabilistic inductive constraint logic, file e309ade4-8c8f-3969-e053-3a05fe0a2c94
|
129
|
|
Causal inference in cplint, file e309ade1-994b-3969-e053-3a05fe0a2c94
|
111
|
|
A Comparison of MCMC Sampling for Probabilistic Logic Programming, file e309ade2-98ec-3969-e053-3a05fe0a2c94
|
105
|
|
The Distribution Semantics for Normal Programs with Function Symbols, file e309ade0-d15b-3969-e053-3a05fe0a2c94
|
98
|
|
Probabilistic Description Logics under the distribution semantics, file e309ade0-86ee-3969-e053-3a05fe0a2c94
|
96
|
|
Learning hierarchical probabilistic logic programs, file e309ade4-85e9-3969-e053-3a05fe0a2c94
|
85
|
|
cplint on SWISH: Probabilistic Logical Inference with a Web Browser, file e309ade2-f13e-3969-e053-3a05fe0a2c94
|
84
|
|
Reasoning with Probabilistic Ontologies, file e309ade1-2ca8-3969-e053-3a05fe0a2c94
|
80
|
|
Probabilistic inductive constraint logic, file e309ade2-d68d-3969-e053-3a05fe0a2c94
|
76
|
|
Studying transaction fees in the Bitcoin Blockchain with probabilistic logic programming, file e309ade2-5c22-3969-e053-3a05fe0a2c94
|
66
|
|
Bandit-based Monte-Carlo structure learning of probabilistic logic programs, file e309ade0-8ed6-3969-e053-3a05fe0a2c94
|
60
|
|
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018, file e309ade2-20d6-3969-e053-3a05fe0a2c94
|
59
|
|
Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach, file e309ade2-0af0-3969-e053-3a05fe0a2c94
|
58
|
|
KRaider: A crawler for linked data, file e309ade2-3fc8-3969-e053-3a05fe0a2c94
|
54
|
|
Probabilistic Declarative Process Mining, file e309ade1-b396-3969-e053-3a05fe0a2c94
|
52
|
|
An Abductive Framework for Datalog± Ontologies, file e309ade0-8be0-3969-e053-3a05fe0a2c94
|
51
|
|
Abductive logic programming for Datalog+/- ontologies, file e309ade0-9b21-3969-e053-3a05fe0a2c94
|
47
|
|
Special Issue on the Italian Conference on Computational Logic: CILC 2009. Preface, file e309ade0-3ce2-3969-e053-3a05fe0a2c94
|
43
|
|
Lifted discriminative learning of probabilistic logic programs, file e309ade1-ec2a-3969-e053-3a05fe0a2c94
|
43
|
|
Guest editors' introduction: Special issue on Inductive Logic Programming (ILP 2012), file e309ade4-aa82-3969-e053-3a05fe0a2c94
|
40
|
|
The Distribution Semantics is Well-Defined for All Normal Programs, file e309ade0-8bde-3969-e053-3a05fe0a2c94
|
39
|
|
Modeling Bitcoin Lightning Network by Logic Programming, file e309ade2-c45f-3969-e053-3a05fe0a2c94
|
37
|
|
Probabilistic Constraint Logic Theories, file e309ade0-d153-3969-e053-3a05fe0a2c94
|
36
|
|
Editorial: Statistical relational artificial intelligence, file e309ade5-34d5-3969-e053-3a05fe0a2c94
|
36
|
|
Tableau Reasoners for Probabilistic Ontologies Exploiting Logic Programming Techniques, file e309ade1-b3c3-3969-e053-3a05fe0a2c94
|
33
|
|
Structure Learning with Distributed Parameter Learning for Probabilistic Ontologies, file e309ade1-bb53-3969-e053-3a05fe0a2c94
|
31
|
|
Using SWISH to realize interactive web-based tutorials for logic-based languages, file e309ade2-1ebf-3969-e053-3a05fe0a2c94
|
31
|
|
Vision inspection with neural networks, file e309ade2-0e27-3969-e053-3a05fe0a2c94
|
29
|
|
Scaling Structure Learning of Probabilistic Logic Programs by MapReduce, file e309ade0-d1b8-3969-e053-3a05fe0a2c94
|
28
|
|
Statistical relational learning for workflow mining, file e309ade0-d633-3969-e053-3a05fe0a2c94
|
28
|
|
Checking Compliance of Execution Traces to Business Rules, file e309ade0-e5a3-3969-e053-3a05fe0a2c94
|
27
|
|
Logic programming techniques for reasoning with probabilistic ontologies, file e309ade0-e39a-3969-e053-3a05fe0a2c94
|
25
|
|
Scaling Structure Learning of Probabilistic Logic Programs by MapReduce, file e309ade2-37cb-3969-e053-3a05fe0a2c94
|
25
|
|
Associate Editor della sezione Frontiers in Machine Learning and Artificial Intelligence della rivista Frontiers in Artificial intelligence, file e309ade2-8e7e-3969-e053-3a05fe0a2c94
|
25
|
|
Reasoning on Datalog± Ontologies with Abductive Logic Programming, file e309ade2-e8ab-3969-e053-3a05fe0a2c94
|
25
|
|
Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities, file 59b40089-722e-4dbc-af66-b185e05ec532
|
22
|
|
A System for Abductive Learning of Logic Programs, file e309ade1-269c-3969-e053-3a05fe0a2c94
|
22
|
|
Statistical Relational Artificial Intelligence, file e309ade5-3989-3969-e053-3a05fe0a2c94
|
22
|
|
Learning Probabilistic Ontologies with Distributed Parameter Learning, file e309ade1-1255-3969-e053-3a05fe0a2c94
|
20
|
|
Analyzing Transaction Fees with Probabilistic Logic Programming, file e309ade2-7952-3969-e053-3a05fe0a2c94
|
20
|
|
MAP Inference in Probabilistic Answer Set Programs, file 72d39ea7-5d62-4f1f-a96d-1c7ba2e1120d
|
17
|
|
A web system for reasoning with probabilistic OWL, file e309ade1-c4e0-3969-e053-3a05fe0a2c94
|
17
|
|
A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics, file e309ade3-04dc-3969-e053-3a05fe0a2c94
|
17
|
|
Abduction with probabilistic logic programming under the distribution semantics, file e309ade5-1bac-3969-e053-3a05fe0a2c94
|
17
|
|
null, file e309ade0-4494-3969-e053-3a05fe0a2c94
|
16
|
|
MAP Inference for Probabilistic Logic Programming, file e309ade2-b69b-3969-e053-3a05fe0a2c94
|
16
|
|
A survey of lifted inference approaches for probabilistic logic programming under the distribution semantics, file e309ade1-2403-3969-e053-3a05fe0a2c94
|
15
|
|
Tableau reasoning for description logics and its extension to probabilities, file e309ade1-cd8a-3969-e053-3a05fe0a2c94
|
15
|
|
A Framework for Reasoning on Probabilistic Description Logics, file e309ade2-fcf9-3969-e053-3a05fe0a2c94
|
15
|
|
Probabilistic logic programming on the web, file e309ade1-45ea-3969-e053-3a05fe0a2c94
|
14
|
|
Learning hierarchical probabilistic logic programs, file e309ade4-a84c-3969-e053-3a05fe0a2c94
|
14
|
|
Dischargeable Obligations in the ScIFF Framework, file 6dc7bb42-0af1-4446-b75c-bf3ddf150d70
|
13
|
|
Abduction with probabilistic logic programming under the distribution semantics, file e309ade5-1bab-3969-e053-3a05fe0a2c94
|
13
|
|
Deductive and Inductive Probabilistic Programming, file e309ade0-d155-3969-e053-3a05fe0a2c94
|
12
|
|
Reasoning on Datalog± Ontologies with Abductive Logic Programming, file e309ade1-b3c5-3969-e053-3a05fe0a2c94
|
12
|
|
A Decision Support System for Food Recycling based on Constraint Logic Programming and Ontological Reasoning, file e309ade1-f3e3-3969-e053-3a05fe0a2c94
|
12
|
|
Preface [to Inductive Logic Programming], file e309ade1-f447-3969-e053-3a05fe0a2c94
|
12
|
|
Dischargeable Obligations in the ScIFF Framework, file e309ade4-b2cf-3969-e053-3a05fe0a2c94
|
12
|
|
Probabilistic Description Logics under the distribution semantics, file e309ade0-fdd2-3969-e053-3a05fe0a2c94
|
11
|
|
Belief Revision by Lamarckian Evolution, file e309ade1-29f1-3969-e053-3a05fe0a2c94
|
11
|
|
Applying Inductive Logic Programming to Process Mining, file e309ade1-2ac5-3969-e053-3a05fe0a2c94
|
11
|
|
Rule-based Programming for Building Expert Systems: a Comparison in the Microbiological Data Validation and Surveillance Domain, file e309ade0-2e08-3969-e053-3a05fe0a2c94
|
10
|
|
cplint on SWISH: Probabilistic Logical Inference with a Web Browser, file e309ade1-b568-3969-e053-3a05fe0a2c94
|
10
|
|
Preface [Up-and-Coming and Short Papers of ILP 2018], file e309ade1-e94b-3969-e053-3a05fe0a2c94
|
10
|
|
Quantum weighted model counting, file e309ade2-b332-3969-e053-3a05fe0a2c94
|
10
|
|
Structure learning of Probabilistic Logic Programs by searching the clause space, file e309ade2-e4ac-3969-e053-3a05fe0a2c94
|
10
|
|
A semantics for Hybrid Probabilistic Logic programs with function symbols, file e309ade3-332c-3969-e053-3a05fe0a2c94
|
10
|
|
Probabilistic inductive constraint logic, file e309ade4-722b-3969-e053-3a05fe0a2c94
|
10
|
|
A probabilistic logic model of Lightning Network, file e309ade5-12c0-3969-e053-3a05fe0a2c94
|
10
|
|
Identification of natural selection in genomic data with deep convolutional neural network, file e309ade5-308c-3969-e053-3a05fe0a2c94
|
10
|
|
Exploiting Parameters Learning for Hyper-parameters Optimization in Deep Neural Networks, file 2b07732f-52ad-4623-bd30-b17b01ca80c8
|
9
|
|
Inference with logic programs with annotated disjunctions under the well founded semantics, file e309ade1-259e-3969-e053-3a05fe0a2c94
|
9
|
|
Inducing Declarative Logic-Based Models from Labeled Traces, file e309ade1-29e5-3969-e053-3a05fe0a2c94
|
9
|
|
Causal inference in cplint, file e309ade1-b92e-3969-e053-3a05fe0a2c94
|
9
|
|
Modeling Bitcoin protocols with probabilistic logic programming, file e309ade2-0e29-3969-e053-3a05fe0a2c94
|
9
|
|
Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach, file e309ade2-3e9a-3969-e053-3a05fe0a2c94
|
9
|
|
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018, file e309ade2-f86a-3969-e053-3a05fe0a2c94
|
9
|
|
Regularization in Probabilistic Inductive Logic Programming, file a4c3710c-17d0-4c90-8de3-368545a06d26
|
8
|
|
null, file e309ade0-b0d9-3969-e053-3a05fe0a2c94
|
8
|
|
Integrating induction and abduction in logic programming, file e309ade1-269f-3969-e053-3a05fe0a2c94
|
8
|
|
Learning the structure of probabilistic logic programs, file e309ade1-b3fe-3969-e053-3a05fe0a2c94
|
8
|
|
Probabilistic inference in SWI-Prolog, file e309ade1-f4a6-3969-e053-3a05fe0a2c94
|
8
|
|
Declarative and Mathematical Programming approaches to Decision Support Systems for food recycling, file e309ade2-b9a2-3969-e053-3a05fe0a2c94
|
8
|
|
An Analysis of Gibbs Sampling for Probabilistic Logic Programs, file e309ade2-c6f1-3969-e053-3a05fe0a2c94
|
8
|
|
Structured Methodology for Clustering Gas Turbine Transients by means of Multi-variate Time Series, file e309ade2-e65c-3969-e053-3a05fe0a2c94
|
8
|
|
Symbolic DNN-Tuner: A Python and ProbLog-based system for optimizing Deep Neural Networks hyperparameters, file e309ade4-d8ea-3969-e053-3a05fe0a2c94
|
8
|
|
Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules, file e309ade4-f8bb-3969-e053-3a05fe0a2c94
|
8
|
|
A Machine Learning Framework for Multi-Hazard Risk Assessment at the Regional Scale in Earthquake and Flood-Prone Areas, file e309ade5-11c0-3969-e053-3a05fe0a2c94
|
8
|
|
Modeling Bitcoin Lightning Network by Logic Programming, file e309ade5-9391-3969-e053-3a05fe0a2c94
|
8
|
|
Symbolic DNN-Tuner, file 66edec32-30e9-404e-9d69-f3793b10c3ad
|
7
|
|
Probabilistic Logic Models for the Lightning Network, file 69bfff7e-c151-4f1b-8191-7020923be9cc
|
7
|
|
null, file e309ade0-3fc3-3969-e053-3a05fe0a2c94
|
7
|
| Totale |
3.796 |