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dc.contributor.advisorDukkipati, Ambedkar
dc.contributor.authorDubey, Abhishek
dc.date.accessioned2018-06-11T05:41:50Z
dc.date.accessioned2018-07-31T04:39:12Z
dc.date.available2018-06-11T05:41:50Z
dc.date.available2018-07-31T04:39:12Z
dc.date.issued2018-06-11
dc.date.submitted2015
dc.identifier.urihttps://etd.iisc.ac.in/handle/2005/3681
dc.identifier.abstracthttp://etd.iisc.ac.in/static/etd/abstracts/4551/G26906-Abs.pdfen_US
dc.description.abstractIn recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better features, which further improves many supervised tasks. These models not only automate the feature extraction process but also provide with robust features for various machine learning tasks. But the unsupervised pretraining and feature extraction using multi-layered networks are restricted only to the input features and not to the output. The performance of many supervised learning algorithms (or models) depends on how well the output dependencies are handled by these algorithms [Dembczy´nski et al., 2012]. Adapting the standard neural networks to handle these output dependencies for any specific type of problem has been an active area of research [Zhang and Zhou, 2006, Ribeiro et al., 2012]. On the other hand, inference into multimodal data is considered as a difficult problem in machine learning and recently ‘deep multimodal neural networks’ have shown significant results [Ngiam et al., 2011, Srivastava and Salakhutdinov, 2012]. Several problems like classification with complete or missing modality data, generating the missing modality etc., are shown to perform very well with these models. In this work, we consider three nontrivial supervised learning tasks (i) multi-class classification (MCC), (ii) multi-label classification (MLC) and (iii) label ranking (LR), mentioned in the order of increasing complexity of the output. While multi-class classification deals with predicting one class for every instance, multi-label classification deals with predicting more than one classes for every instance and label ranking deals with assigning a rank to each label for every instance. All the work in this field is associated around formulating new error functions that can force network to identify the output dependencies. Aim of our work is to adapt neural network to implicitly handle the feature extraction (dependencies) for output in the network structure, removing the need of hand crafted error functions. We show that the multimodal deep architectures can be adapted for these type of problems (or data) by considering labels as one of the modalities. This also brings unsupervised pretraining to the output along with the input. We show that these models can not only outperform standard deep neural networks, but also outperform standard adaptations of neural networks for individual domains under various metrics over several data sets considered by us. We can observe that the performance of our models over other models improves even more as the complexity of the output/ problem increases.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesG26906en_US
dc.subjectNeural Networksen_US
dc.subjectDeep Neural Network Modelsen_US
dc.subjectNeural Network Architectureen_US
dc.subjectMultimodal Deep Neural Networksen_US
dc.subjectMultimodal Deep Learningen_US
dc.subjectMulti-Label Classification (MLC)en_US
dc.subjectMulti-class Classification (MCC)en_US
dc.subjectLabel Rankingen_US
dc.subjectMultimodal Neural Networksen_US
dc.subjectSupervised Learningen_US
dc.subjectMultilayer Neural Networken_US
dc.subjectPerceptron Modelen_US
dc.subject.classificationComputer Scienceen_US
dc.titleMultimodal Deep Learning for Multi-Label Classification and Ranking Problemsen_US
dc.typeThesisen_US
dc.degree.nameMSc Enggen_US
dc.degree.levelMastersen_US
dc.degree.disciplineFaculty of Engineeringen_US


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