'Feature learning' diaporamas de présentation

Deep Learning & Feature Learning Methods for Vision

687 views • 67 slides

Multimodal Deep Learning

Multimodal Deep Learning. Jiquan Ngiam Aditya Khosla , Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng Stanford University. McGurk Effect. Audio-Visual Speech Recognition. Feature Challenge. Classifier (e.g. SVM). Representing Lips.

478 views • 46 slides

A Machine Learning Approach for Automatic Student Model Discovery

A Machine Learning Approach for Automatic Student Model Discovery. Nan Li, N oboru Matsuda, William Cohen, and Kenneth Koedinger Computer Science Department Carnegie Mellon University. Student Model. A set of knowledge components ( KCs )

411 views • 29 slides

A Machine Learning Approach for Automatic Student Model Discovery

A Machine Learning Approach for Automatic Student Model Discovery. Nan Li, N oboru Matsuda, William Cohen, and Kenneth Koedinger Computer Science Department Carnegie Mellon University. Student Model. A set of knowledge components ( KCs )

349 views • 29 slides

Li Deng Microsoft Research, Redmond, USA Tianjin University, July 2-5, 2013

Deep Learning for Information Processing & Artificial Intelligence New-Generation Models & Methodology for Advancing AI & SIP. Li Deng Microsoft Research, Redmond, USA Tianjin University, July 2-5, 2013 (including joint work with colleagues at MSR, U of Toronto, etc.).

437 views • 29 slides

Recent Developments in Deep Learning

Recent Developments in Deep Learning. Quoc V. Le Stanford University and Google. Purely supervised. Quoc V. Le. Deep Learning. Almost abandoned between 2000-2006 - Overfitting , slow, many local minima, gradient vanishing

287 views • 28 slides

Presented by: Mingyuan Zhou Duke University, ECE June 17, 2011

An Analysis of Single-Layer Networks in Unsupervised Feature Learning Adam Coates, Honglak Lee and Andrew Y. Ng AISTATS 2011 The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization Adam Coates and Andrew Y. Ng ICML 2011. Presented by: Mingyuan Zhou

235 views • 23 slides

Convolutional Decision Trees

Convolutional Decision Trees. Dmitry Laptev, Joachim M. Buhmann Machine Learning Lab, ETH Zurich. Connectomics *. * Reconstruction and study of Connectome : a map of neuron connections. Connectomics. Hard to automate On a whole-brain scale it is simply necessary Good techniques are:

365 views • 21 slides

CS228: Deep Learning & Unsupervised Feature Learning

CS228: Deep Learning & Unsupervised Feature Learning. Andrew Ng. Pieter Abbeel Adam Coates Zico Kolter Ian Goodfellow Quoc Le Honglak Lee Rajat Raina Andrew Saxe. TexPoint fonts used in EMF.

419 views • 9 slides