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

Natural Language to Structured Query Generation via Meta-Learning Po-Sen Huang 1 Chenglong Wang 2 Rishabh Singh 3 * Wen-tau Yih 4 Xiaodong He 5 * 1. Introduction Conventional supervised training is a pervasive paradigm

Source URL: uclmr.github.io

• Machine learning
• Learning
• Artificial intelligence
• Meta learning
• Support vector machine
• Supervised learning
• Training
• test
• and validation sets

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Semi-Supervised Transfer Learning Using Marginal Predictors Deshmukh, A.; Laftchiev, E.

Source URL: www.merl.com

Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning Dan Garrette Chris Dyer Jason Baldridge

Source URL: www.dhgarrette.com

Online Semi-Supervised Learning on Quantized Graphs Michal Valko Branislav Kveton Computer Science Department Intel Labs

Source URL: www.bkveton.com

Semi-Supervised Learning with the Deep Rendering Mixture Model Tan Nguyen1,2 Wanjia Liu1 Ethan Perez1 Richard G. Baraniuk1

Source URL: ankitbpatel715.files.wordpress.com

A Supertag-Context Model for Weakly-Supervised CCG Parser Learning Dan Garrette∗ Chris Dyer†

Source URL: www.dhgarrette.com

A human motion feature based on semi-supervised learning of GMM

Source URL: eprints.bournemouth.ac.uk

A Deep Probabilistic Framework for Heterogeneous Self-Supervised Learning of Affordances Atabak Dehban1,2 , Lorenzo Jamone3,1 , Adam R. Kampff4 and Jos´e Santos-Victor1 Abstract— The perception of affordances provides

Source URL: vislab.isr.ist.utl.pt

1 CS229 Problem Set #1 CS 229, Fall 2017 Problem Set #1: Supervised Learning

Source URL: cs229.stanford.edu