Deep Learning for Information Processing & Artificial Intelligence New-Generation Models & Methodology for Advancing AI & SIP Li Deng Microsoft Research, Redmond, USA Tianjin University, July 4, 2013 (Day 3) (including joint work with colleagues at MSR, U of Toronto, etc.) DAY Three: July 4, 2013 Various Topics: Computational neuroscience; connections to deep/recurrent NN; Convolutional NN in vision and speech; Hopfield net and Boltzmann machines; NLP, and IR applications, etc. 2 New deep learning video posted today: http://www.icassp2013.com/PlenarySpeakers.asp 3 What Types of Problems Fit (not fit) Deep Learning (some conjectures) Perceptual AI e.g.: Image/video recognition Speech recognition Speech/text understanding Sequential data with temporal structure (stock market prediction?)
Data matching e.g.: Malware detection(ICASSP2013) movie recommender, speaker/language detection? Non-obvious data representations Easy data representation e.g., histogram of events, user-watched movies, etc. Deep learning already shows tremendous benefits Deep learning may not win over standard machine learning Computational Neuroscience (coursera) Hebbian learning Hopfield Net, Bolzmann machines, memory models Bio-inspired AI RNN Computer vision (LeCun slides)
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