The Experts below are selected from a list of 136719 Experts worldwide ranked by ideXlab platform
Carsten Marr - One of the best experts on this subject based on the ideXlab platform.
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evaluation of Domain adaptation approaches for robust classification of heterogeneous biological data sets
International Conference on Artificial Neural Networks, 2019Co-Authors: M Schneider, Lichao Wang, Carsten MarrAbstract:Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful for cases where only a small amount of training data is available in the Domain of Interest, while a large amount of training data is available in a different, but relevant Domain.
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evaluation of Domain adaptation approaches for robust classification of heterogeneous biological data sets
bioRxiv, 2019Co-Authors: M Schneider, Lichao Wang, Carsten MarrAbstract:Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful for cases where only a small amount of training data is available in the Domain of Interest, while a large amount of training data is available in a different, but relevant Domain. We investigated to what extent Domain adaptation was able to improve prediction accuracy for complex biological data. To that end, we used simulated data and time-lapse movies of differentiating blood stem cells in different cell cycle stages from multiple experiments and compared three commonly used Domain adaptation approaches. EasyAdapt, a simple technique of structured pooling of related data sets, was able to improve accuracy when classifying the simulated data and cell cycle stages from microscopic images. Meanwhile, the technique proved robust to the potential negative impact on the classification accuracy that is common in other techniques that build models with heterogeneous data. Despite its implementation simplicity, EasyAdapt consistently produced more accurate predictions compared to conventional techniques. Domain adaptation is therefore able to substantially reduce the amount of work required to create a large amount of annotated training data in the Domain of Interest necessary whenever the Domain changes even a little, which is common not only in biological experiments, but universally exists in almost all data collection routines.
M Schneider - One of the best experts on this subject based on the ideXlab platform.
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evaluation of Domain adaptation approaches for robust classification of heterogeneous biological data sets
International Conference on Artificial Neural Networks, 2019Co-Authors: M Schneider, Lichao Wang, Carsten MarrAbstract:Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful for cases where only a small amount of training data is available in the Domain of Interest, while a large amount of training data is available in a different, but relevant Domain.
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evaluation of Domain adaptation approaches for robust classification of heterogeneous biological data sets
bioRxiv, 2019Co-Authors: M Schneider, Lichao Wang, Carsten MarrAbstract:Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful for cases where only a small amount of training data is available in the Domain of Interest, while a large amount of training data is available in a different, but relevant Domain. We investigated to what extent Domain adaptation was able to improve prediction accuracy for complex biological data. To that end, we used simulated data and time-lapse movies of differentiating blood stem cells in different cell cycle stages from multiple experiments and compared three commonly used Domain adaptation approaches. EasyAdapt, a simple technique of structured pooling of related data sets, was able to improve accuracy when classifying the simulated data and cell cycle stages from microscopic images. Meanwhile, the technique proved robust to the potential negative impact on the classification accuracy that is common in other techniques that build models with heterogeneous data. Despite its implementation simplicity, EasyAdapt consistently produced more accurate predictions compared to conventional techniques. Domain adaptation is therefore able to substantially reduce the amount of work required to create a large amount of annotated training data in the Domain of Interest necessary whenever the Domain changes even a little, which is common not only in biological experiments, but universally exists in almost all data collection routines.
Qiang Yang - One of the best experts on this subject based on the ideXlab platform.
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A Survey on Transfer Learning
IEEE Transactions on Knowledge and Data Engineering, 2010Co-Authors: Qiang YangAbstract:A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one Domain of Interest, but we only have sufficient training data in another Domain of Interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as Domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.
Diego Calvanese - One of the best experts on this subject based on the ideXlab platform.
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ontop answering sparql queries over relational databases
Social Work, 2016Co-Authors: Diego Calvanese, Enjami Cogrel, Sarah Komlaebri, Roma Kontchakov, Davide Lanti, Marti Rezk, Mariano Rodriguezmuro, Guohui XiaoAbstract:We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the Domain of Interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases.
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Verification of Inconsistency-Aware Knowledge and Action Bases ⇤
2013Co-Authors: Diego Calvanese, Marco Montali, Ario Santoso, Evgeny Kharlamov, Dmitriy ZheleznyakovAbstract:Description Logic Knowledge and Action Bases (KABs) have been recently introduced as a mechanism that provides a semantically rich representation of the information on the Domain of Interest in terms of a DL KB and a set of actions to change such information over time, possibly introducing new objects. In this setting, decidability of verification of sophisticated temporal properties over KABs, expressed in a variant of first-order µ-calculus, has been shown. However, the established framework treats inconsistency in a simplistic way, by rejecting inconsistent states produced through action execution. We address this problem by showing how inconsistency handling based on the notion of repairs can be integrated into KABs, resorting to inconsistency-tolerant semantics. In this setting, we establish decidability and complexity of verification.
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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Verification of Inconsistency-Aware Knowledge and Action Bases ∗
2013Co-Authors: Diego Calvanese, Marco Montali, Ario Santoso, Evgeny Kharlamov, Dmitriy ZheleznyakovAbstract:Description Logic Knowledge and Action Bases (KABs) have been recently introduced as a mechanism that provides a semantically rich representation of the information on the Domain of Interest in terms of a DL KB and a set of actions to change such information over time, possibly introducing new objects. In this setting, decidability of verification of sophisticated temporal properties over KABs, expressed in a variant of first-order µ-calculus, has been shown. However, the established framework treats inconsistency in a simplistic way, by rejecting inconsistent states produced through action execution. We address this problem by showing how inconsistency handling based on the notion of repairs can be integrated into KABs, resorting to inconsistency-tolerant semantics. In this setting, we establish decidability and complexity of verification.
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On Inconsistency-Aware Knowledge and Action Bases: Semantics and Verification ∗
2013Co-Authors: Diego Calvanese, Marco Montali, Ario Santoso, Evgeny Kharlamov, Dmitriy ZheleznyakovAbstract:Description Logic Knowledge and Action Bases (KABs) have been recently introduced as a mechanism that provides a semantically rich representation of the information on the Domain of Interest in terms of a DL KB and a set of actions to change such information over time, possibly introducing new objects. In this setting, decidability of verification of sophisticated temporal properties over KABs, expressed in a variant of first-order µ-calculus, has been shown. However, the established framework treats inconsistency in a simplistic way, by rejecting inconsistent states produced through action execution. We address this problem by showing how inconsistency handling based on the notion of repairs can be integrated into KABs, resorting to inconsistency-tolerant semantics. In this setting, we establish decidability and complexity of verification.
Lichao Wang - One of the best experts on this subject based on the ideXlab platform.
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evaluation of Domain adaptation approaches for robust classification of heterogeneous biological data sets
International Conference on Artificial Neural Networks, 2019Co-Authors: M Schneider, Lichao Wang, Carsten MarrAbstract:Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful for cases where only a small amount of training data is available in the Domain of Interest, while a large amount of training data is available in a different, but relevant Domain.
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evaluation of Domain adaptation approaches for robust classification of heterogeneous biological data sets
bioRxiv, 2019Co-Authors: M Schneider, Lichao Wang, Carsten MarrAbstract:Most machine learning algorithms require that training data are identically distributed to ensure effective learning. In biological studies, however, even small variations in the experimental setup can lead to substantial deviations. Domain adaptation offers tools to deal with this problem. It is particularly useful for cases where only a small amount of training data is available in the Domain of Interest, while a large amount of training data is available in a different, but relevant Domain. We investigated to what extent Domain adaptation was able to improve prediction accuracy for complex biological data. To that end, we used simulated data and time-lapse movies of differentiating blood stem cells in different cell cycle stages from multiple experiments and compared three commonly used Domain adaptation approaches. EasyAdapt, a simple technique of structured pooling of related data sets, was able to improve accuracy when classifying the simulated data and cell cycle stages from microscopic images. Meanwhile, the technique proved robust to the potential negative impact on the classification accuracy that is common in other techniques that build models with heterogeneous data. Despite its implementation simplicity, EasyAdapt consistently produced more accurate predictions compared to conventional techniques. Domain adaptation is therefore able to substantially reduce the amount of work required to create a large amount of annotated training data in the Domain of Interest necessary whenever the Domain changes even a little, which is common not only in biological experiments, but universally exists in almost all data collection routines.