Differentiable Pooling for Hierarchical Feature Learning Matthew D. Zeiler and Rob Fergus Dept. of Computer Science, Courant Institute, New York University arXiv:1207.0151v1 [cs.CV] 30 Jun 2012

Source URL: www.matthewzeiler.com

CS 558: Homework Assignment 4 - Visual Recognition Due: April 26, 11:59pm Philippos Mordohai Department of Computer Science Stevens Institute of Technology

Source URL: www.cs.stevens.edu

• Artificial intelligence
• Image processing
• Statistics
• Nonparametric statistics
• Feature detection
• Vision
• Computer vision
• Machine learning
• K-nearest neighbors algorithm
• Image histogram
• Statistical classification
• Histogram

Optimal rates for random Fourier feature approximations∗ Zolt´an Szab´o (Gatsby Unit, University College London)† Abstract: Kernel methods represent one of the most powerful tools in machine learning to tackle

Source URL: www.gatsby.ucl.ac.uk

Class Generative Models based on Feature Regression for Pose Estimation of Object Categories Michele Fenzi, Laura Leal-Taixé, Bodo Rosenhahn, Jörn Ostermann Institute for Information Processing (TNT), Leibniz Universit

Source URL: www.tnt.uni-hannover.de

• Artificial intelligence
• Statistics
• Computer vision
• Robot control
• Econometrics
• Machine learning
• 3D single-object recognition
• Outline of object recognition
• Pose
• Cross-validation
• Regression analysis

AMRITA CEN@SemEval-2015: Paraphrase Detection for Twitter using ¯ Unsupervised Feature Learning with Recursive Autoencoders Mahalakshmi Shanmuga Sundaram, Anand Kumar Madasamy and Soman Kotti Padannayil Center for Excel

Source URL: alt.qcri.org

Recognizing Actions from Still Images Nazli Ikizler, R. Gokberk Cinbis, Selen Pehlivan and Pinar Duygulu Bilkent University, Dept of Computer Engineering, 06800, Ankara, Turkey {inazli, cinbis, pselen, duygulu}@bilkent.e

Source URL: web.cs.hacettepe.edu.tr

• Formal sciences
• Artificial intelligence
• Science and technology
• Vision
• Image processing
• Computational neuroscience
• Machine learning
• Feature detection
• Histogram of oriented gradients
• Outline of object recognition
• Conference on Computer Vision and Pattern Recognition
• Computer vision

Discriminative Learning of Deep Convolutional Feature Point Descriptors Edgar Simo-Serra∗,1,5 , Eduard Trulls∗,2,5 , Luis Ferraz3 Iasonas Kokkinos4 , Pascal Fua2 , Francesc Moreno-Noguer5 1 Waseda University, Tokyo,

Source URL: www.cv-foundation.org

• Feature detection
• Speeded up robust features
• Descriptor
• Visual descriptor
• Feature
• Structure from motion
• Outline of object recognition
• Vision

Journal of Machine Learning Research X (YYYYSubmitted 02/06; Published Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation

Source URL: www.cs.technion.ac.il

• Information science
• Information
• Knowledge representation
• Natural language processing
• Artificial intelligence
• Machine learning
• Statistical classification
• Data mining
• Evgeniy Gabrilovich
• Naive Bayes classifier
• Bag-of-words model
• Document classification

Application of Clustering for Unsupervised Language Learning Jeremy Hoffman and Omkar Mate Abstract We describe a method for automatically learning word similarity from a corpus. We constructed feature vectors for words

Source URL: cs229.stanford.edu

arXiv:1402.3337v2 [stat.ML] 10 NovZero-bias autoencoders and the benefits of co-adapting features Roland Memisevic

Source URL: arxiv.org

• Artificial neural networks
• Machine learning
• Learning
• Artificial intelligence
• Unsupervised learning
• Autoencoder
• Computational neuroscience
• Computational statistics
• Feature learning
• Rectifier
• Restricted Boltzmann machine
• Supervised learning