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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Online Learning for Matrix Factorization and Sparse Coding
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
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A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labelled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text.This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated "all-purpose" inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.