The last few years has seen a flurry of activity in non-convex approaches to enable solution of large scale optimization problems that come up in machine learning. The common thread in many of these results is that low-r

Source URL: mmds-data.org

An Implementation for Fault Tolerance and Experimental Results Frithjof Dau, SAP AG Abstract. Even for small or medium sized contexts, the corresponding concept lattice soon becomes too large to be nicely displayed and u

Source URL: www.dr-dau.net

• Algebra
• Abstract algebra
• Geometry
• Lattice theory
• Lie groups
• Data mining
• Formal concept analysis
• Machine learning
• Ontology
• Lattice

Static vs. Dynamic Modeling of Human Nonverbal Behavior from Multiple Cues and Modalities Stavros Petridis Hatice Gunes

Source URL: ibug.doc.ic.ac.uk

• Artificial intelligence
• Learning
• Humancomputer interaction
• Automatic identification and data capture
• Machine learning
• Affective computing
• Face recognition
• Parts of speech
• Speech recognition
• Nonverbal communication
• Statistical classification
• Classifier

Query Word Labeling using Supervised Machine Learning: Shared task report by PESIT team Channa Bankapur Adithya Abraham Philip

Source URL: www.isical.ac.in

• Linguistics
• Language
• Devanagari
• Hindi
• Hindustani orthography
• Schwa
• Alphabet
• Hangul

STRUCTURED DISCRIMINATIVE MODELS USING DEEP NEURAL-NETWORK FEATURES R. C. van Dalen, J. Yang, H. Wang, A. Ragni, C. Zhang, M. J. F. Gales Department of Engineering, University of Cambridge, United Kingdom ABSTRACT State-

Source URL: mi.eng.cam.ac.uk

• Markov models
• Machine learning
• Artificial intelligence
• Learning
• Computational linguistics
• Speech recognition software
• Computer accessibility
• Speech recognition
• Hidden Markov model
• Conditional random field
• Language model
• Image segmentation

Clustered factor analysis of multineuronal spike data Lars Buesing1 , Timothy A. Machado1,2 , John P. Cunningham1 and Liam Paninski1 1 Department of Statistics, Center for Theoretical Neuroscience & Grossman Center for

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

• Statistics
• Applied mathematics
• Machine learning
• Cluster analysis
• Cybernetics
• Dimension reduction
• Computational neuroscience
• Mixture model
• K-means clustering
• Expectationmaximization algorithm
• Principal component analysis
• Artificial neural network

Latent Trees for Estimating Intensity of Facial Action Units Sebastian Kaltwang Imperial College London Sinisa Todorovic Oregon State University

Source URL: ibug.doc.ic.ac.uk

• Machine learning
• Statistics
• Learning
• Graphical models
• Conditional random field
• Support vector machine
• Expectationmaximization algorithm

Music and lazy learning Ichiro Fujinaga Peabody Institute, Johns Hopkins University 1 E. Mt. Vernon Place Baltimore, MarylandU. S. A.

Source URL: www.music.mcgill.ca

• Machine learning
• Artificial intelligence
• Cognitive science
• Cognition
• Artificial neural network
• Intelligence
• Pattern recognition
• Computational learning theory
• Supervised learning
• Word-sense disambiguation
• Case-based reasoning
• Vasant Honavar

Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves

Source URL: ijcai.org

• Artificial intelligence
• Computational neuroscience
• Artificial neural networks
• Applied mathematics
• Cognitive science
• Machine learning
• Cybernetics
• Model selection
• Convolutional neural network
• Hyperparameter optimization
• Deep learning
• MNIST database

Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 JulySOLVING JAPANESE PUZZLES WITH LOGICAL RULES AND DEPTH FIRST SEARCH ALGORITHM MIN-QUAN JING, CHIUNG-HSUEH Y

Source URL: debut.cis.nctu.edu.tw

• Logic puzzles
• NP-complete problems
• Logic
• Mathematics
• Abstraction
• Nonogram
• Light Up
• Algorithm
• Sudoku solving algorithms
• Nurikabe