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

Object Support in an Array-Based GPGPU Extension for Ruby Matthias Springer Hidehiko Masuhara Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Japan

Source URL: m-sp.org

• Computing
• Software engineering
• Computer programming
• Parallel computing
• Graphics hardware
• Object-oriented programming languages
• GPGPU
• Cross-platform software
• CUDA
• General-purpose computing on graphics processing units
• AOS and SOA
• Thread

Poster: Identify and Track Web Attacker Based on Deceptive Technology and Browser Fingerprint Heyang Lv1 Binxing Fang2 Xiang Cui3,4 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Source URL: www.ieee-security.org

• Computing
• Fingerprinting algorithms
• Cryptography
• Identification
• Internet privacy
• Biometrics
• Computer network security
• Device fingerprint
• Internet fraud
• Public key fingerprint
• Fingerprint
• Tor

Social and Behavioral Sciences Team Annual Report Executive Office of the President National Science and Technology Council

Source URL: sbst.gov

• Science and technology in the United States
• General Services Administration
• Social and Behavioral Sciences Team
• Education
• Human behavior
• Science
• Fellows of the American Association for the Advancement of Science
• Executive Office of the President of the United States
• Office of Science and Technology Policy
• National Science and Technology Council
• Maya Shankar
• Science
• technology
• engineering
• and mathematics

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

WYDZIA£ HODOWLI I BIOLOGII ZWIERZĄT FACULTY OF ANIMAL BREEDING AND BIOLOGY UNIWERSYTET TECHNOLOGICZNO-PRZYRODNICZY UNIVERSITY OF TECHNOLOGY AND LIFE SCIENCES

Source URL: whbz.utp.edu.pl

Frank E. Ritter College of Information Sciences and Technology Penn State University 316G IST Building University Park, PA 16802 +FAX)

Source URL: www.frankritter.com

Dr. Xian Meng Institute of Mechanics, Chinese Academy of Sciences, China Xian Meng received a bachelor’s degree and a master’s degree in applied physics from Dalian University of Technology in 1997 andShe comp

Source URL: icmap-2018.org