закладки (спрятать)574
- AgriDrupal is both a “suite of solutions” for agricultural information management and dissemination, built on the Drupal CMS, and the community of practice around these solutions.
- AIDA is a framework and online tool for entity detection and disambiguation. Given a natural-language text or a Web table, it maps mentions of ambiguous names onto canonical entities (e.g., individual people or places) registered in the YAGO2 knowledge base.
- The consumption of Linked Data is a task of increasing complexity on the Web of Data. This complexity is due to several factors, including the sheer size of the Web of Data, the diversity of vocabularies used to describe this data and the lack of schema information in knowledge bases. ALOE provide a semi-automatic solution to address this problem.
- The DL-Learner software learns concepts in Description Logics (DLs) from examples. It extends Inductive Logic Programming to Descriptions Logics and the Semantic Web.
- FOX is a framework that integrates the Linked Data Cloud and makes uses of the diversity of NLP algorithms to extract RDF triples of high accuracy out of NL. In its current version, it integrates and merges the results of Named Entity Recognition, Keyword Extraction and Relation Extraction tools.
- LIMES is a link discovery framework for the Web of Data. It implements time-efficient approaches for large-scale link discovery based on the characteristics of metric spaces. It is easily configurable via a web interface. It can also be downloaded as standalone tool for carrying out link discovery locally.
- Homepage for Algorithms for Massive Data Sets at DTU Informatics
- A great deal of research has focused on algorithms for learning features from un- labeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning al- gorithms and deep models. In this paper, however, we show that several very sim- ple factors, such as the number of hidden nodes in the model, may be as important to achieving high performance as the choice of learning algorithm or the depth of the model. Specifically, we will apply several off-the-shelf feature learning al- gorithms (sparse auto-encoders, sparse RBMs and K-means clustering, Gaussian mixtures) to NORB and CIFAR datasets using only single-layer networks. We then present a detailed analysis of the effect of changes in the model setup: the receptive field size, number of hidden nodes (features), the step-size (“stride”) be- tween extracted features, and the effect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are as critical to achieving high performance as the choice of algorithm itself—so critical, in fact, that when these parameters are pushed to their limits, we are able to achieve state-of-the- art performance on both CIFAR and NORB using only a single layer of features. More surprisingly, our best performance is based on K-means clustering, which is extremely fast, has no hyper-parameters to tune beyond the model structure it- self, and is very easy implement. Despite the simplicity of our system, we achieve performance beyond all previously published results on the CIFAR-10 and NORB datasets (79.6% and 97.0% accuracy respectively).
- a bookmark
- This is androidpatterns.com, a set of interaction patterns that can help you design Android apps. An interaction pattern is a short hand summary of a design solution that has proven to work more than once. Please be inspired: use them as a guide, not as a law.
- Details on font rendering for different OS.
- rdfstore-js - JS RDF store with SPARQL support
publications
(hide)335
This list of posts may not be accurate to recent changes. If you want accurate posts, but with limited sorting follow this link.
- Proceedings of the 14th International Conference on Machine Learning (ICML-97), page 412--420. Morgan Kaufmann, (1997)
- International Workshop on Inductive Logic Programming, page 291-299. (1998)
- (1998)
- Proceedings of the 2009 International Database Engineering & Applications Symposium, page 31--42. New York, NY, USA, ACM, (2009)
- (2003)
- VLDB, page 81-92. (2003)
- MIT-AI Laboratory, Memo 306, (1974)
- 15th European Conference on Artificial Intelligence (ECAI'02): Workshop on Machine Learning and Natural Language Processing for Ontology Engineering, Lyon, France, (2002)
- SAC '05: Proceedings of the 2005 ACM symposium on Applied computing, page 511--518. New York, NY, USA, ACM Press, (2005)
- WWW (Alternate Track Papers & Posters), page 296-297. ACM, (2004)
- PKDD, volume 1510 of Lecture Notes in Computer Science, page 19-27. Springer, (1998)
- J. Artif. Intell. Res. (JAIR), (2000)
- ICANN (2), volume 3697 of Lecture Notes in Computer Science, page 583-588. Springer, (2005)
- IJCAI03 Workshop on Learning Statistical Models from Relational Data, (2003)
- Artif. Intell. Rev., 18 (2): 77-95 (2002)
- SIGIR, page 3-12. ACM/Springer, (1994)
- Electronic Notes in Theoretical Computer Science, (2004)Proceedings of the joint meeting of the 6th Conference on Formal Grammar and the 7th Conference on Mathematics of Language.
- AAAI/IAAI, page 335-342. (1999)
- The VLDB Journal, (2001)
- The Knowledge Engineering Review, 0 (0): 1-24 (2005)
