A deep-learning model-based and data-driven hybrid architecture for image annotation
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Deep adaptive networks for image classification
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Active deep networks for semi-supervised sentiment classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Sentiment classification based on supervised latent n-gram analysis
Proceedings of the 20th ACM international conference on Information and knowledge management
Sentiment classification with supervised sequence embedding
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Internet of Things (IoT): A vision, architectural elements, and future directions
Future Generation Computer Systems
Deep learning of representations: looking forward
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae. Each level of the architecture represents features at a different level of abstraction, defined as a composition of lower-level features. Searching the parameter space of deep architectures is a difficult task, but new algorithms have been discovered and a new sub-area has emerged in the machine learning community since 2006, following these discoveries. Learning algorithms such as those for Deep Belief Networks and other related unsupervised learning algorithms have recently been proposed to train deep architectures, yielding exciting results and beating the state-of-the-art in certain areas. Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area.