Distributed Machine Learning Maria-Florina Balcan Carnegie Mellon University Distributed Machine Learning Modern applications: m a s s i v e a m o u n t s of data

Source URL: grigory.us

Under review as a conference paper at ICLRR EINFORCEMENT L EARNING N EURAL T URING M ACHINES - R EVISED Ilya Sutskever2 Google Brain

Source URL: arxiv.org

• Artificial neural networks
• Applied mathematics
• Computational neuroscience
• Machine learning
• Academia
• Cybernetics
• Computational statistics
• Mathematical psychology
• Long short-term memory
• Backpropagation
• Reinforcement learning
• Artificial neuron

Eavesdropping on Electronic Guidebooks: Observing Learning Resources in Shared Listening Environments Allison Woodruff, Paul M. Aoki, Rebecca E. Grinter, Amy Hurst, Margaret H. Szymanski, and James D. Thornton Palo Alto

Source URL: www.allisonwoodruff.com

Modeling Annotated Data David M. Blei Michael I. Jordan Division of Computer Science

Source URL: www.cs.princeton.edu

• Statistics
• Probability
• Artificial intelligence
• Statistical natural language processing
• Machine learning
• Statistical models
• Latent Dirichlet allocation
• Probability distributions
• Mixture model
• Expectationmaximization algorithm
• Variational Bayesian methods
• Automatic image annotation

Wavelets on Graphs via Deep Learning Raif M. Rustamov & Leonidas Guibas Computer Science Department, Stanford University {rustamov,guibas}@stanford.edu

Source URL: geometry.stanford.edu

• Wavelets
• Signal processing
• Functional analysis
• Timefrequency analysis
• Digital signal processing
• Lifting scheme
• Discrete wavelet transform
• Haar wavelet
• Diffusion wavelets
• Daubechies wavelet
• Fast wavelet transform

Learning annotated hierarchies from relational data Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, and Joshua B. Tenenbaum CSAIL, Dept. of Brain & Cognitive Sciences, MIT, Cambridge, MA 02139 {droy, ckemp, vkm, jbt}

Source URL: danroy.org

EDUCATION AS THE PRACTICE OF FREEDOM PROJECT Gina English Tillis M.A. office JM101 email: First-Year Experience Coordinator PLC email: …@htu.edu Peer Learning Consultant Mentor “i entered the classro

Source URL: htfye.com

Supplemental Material for Entropy and Margin Maximization for Structured Output Learning Patrick Pletscher, Cheng Soon Ong, and Joachim M. Buhmann Department of Computer Science, ETH Z¨ urich, Switzerland

Source URL: www.pletscher.org

Learning Cluster Analysis through Experience Joshua M. Lewis Virginia R. de Sa

Source URL: mindmodeling.org

PROBE AND ADJUST IN INFORMATION TRANSFER GAMES SIMON M. HUTTEGGER, BRIAN SKYRMS, AND KEVIN J. S. ZOLLMAN Abstract. We study a low-rationality learning dynamics called probe and adjust. Our emphasis is on its properties i

Source URL: www.andrew.cmu.edu