Estimating the strength of unlabeled information during semi-supervised learning Brenden M. Lake () Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology James L. McClelland (m

Source URL: cims.nyu.edu

• Machine learning
• Semi-supervised learning
• Labeled data
• Supervised learning

Learning Linguistic Descriptors of User Roles in Online Communities Alex Wang1 , William L. Hamilton2 , Jure Leskovec2 1 School of Engineering and Applied Sciences, Harvard University, Cambridge MA, Computer Scie

Source URL: www.aclweb.org

• Statistical natural language processing
• World Wide Web
• Digital media
• Internet culture
• Subreddits
• Crowdsourcing
• Reddit
• Collaboration
• /r/IAmA
• Oriented FAST and rotated BRIEF
• Topic model
• Select

One shot learning of simple visual concepts Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Abstract

Source URL: cims.nyu.edu

• Languages of East Asia
• Artificial neural networks
• Chinese characters
• Korean language
• Languages of Asia
• Artificial intelligence
• Market research
• Stroke
• One-shot learning
• Deep learning
• MNIST database
• Symbol

Discovering Structure by Learning Sparse Graphs Brenden M. Lake and Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology {brenden, jbt}@mit.edu Abstract

Source URL: cims.nyu.edu

• Machine learning
• Artificial intelligence
• Learning
• Mathematics
• Sparse approximation
• Semantic memory
• Regularization
• Supervised learning
• Semantic network
• Sparse matrix
• Dense graph

Learning-Based Abstractions for Nonlinear Constraint Solving Sumanth Dathathri ∗1 , Nikos Arechiga2 , Sicun Gao3 , and Richard M. Murray1 1 Computing and Mathematical Sciences, California Institute of Technology 2

Source URL: scungao.github.io

• Constraint programming
• Mathematical optimization
• Theoretical computer science
• Statistical classification
• Artificial intelligence
• Machine learning
• Support vector machine
• Constraint satisfaction problem
• Constraint satisfaction
• Decomposition method
• Constrained optimization
• Constraint

Learning-Based Testing the Sliding Window Behavior of TCP Implementations Paul Fiter˘au-Bro¸stean?1 and Falk Howar2 1 Institute for Computing and Information Sciences, Radboud University, Nijmegen,

Source URL: www.falkhowar.de

• Transmission Control Protocol
• Constructible universe
• Algorithm
• Sliding window protocol
• Mathematics
• Dimension reduction
• Computing
• Philosophy of mathematics

College of Sciences Academic Learning Compacts Chemistry - B.S. Discipline Specific Knowledge, Skills, Behavior and Values 1. Graduates of our program will have knowledge of chemistry which

Source URL: oeas.ucf.edu

College of Medicine Academic Learning Compacts Medical Laboratory Sciences - B.S. Discipline Specific Knowledge, Skills, Behavior and Values 1. MLS graduates will demonstrate in-depth knowledge of analytical techniques a

Source URL: oeas.ucf.edu

No More Pesky Learning Rates Tom Schaul Sixin Zhang Yann LeCun Courant Institute of Mathematical Sciences

Source URL: schaul.site44.com

Deep Learning for Emotion Recognition on Small Datasets Using Transfer Learning Hong-Wei Ng, Viet Dung Nguyen, Vassilios Vonikakis, Stefan Winkler Advanced Digital Sciences Center (ADSC) University of Illinois at Urbana-

Source URL: vintage.winklerbros.net