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

On First-Order Meta-Learning Algorithms arXiv:1803.02999v3 [cs.LG] 22 Oct 2018 Alex Nichol and Joshua Achiam and John Schulman OpenAI

Source URL: arxiv.org

• Numerical analysis
• Mathematical analysis
• Mathematical optimization
• Generalized functions
• Distribution
• Functional analysis
• Stochastic gradient descent
• Gradient descent

Natural Language to Structured Query Generation via Meta-Learning Po-Sen Huang? , Chenglong Wang† , Rishabh Singh? , Wen-tau Yih‡ , Xiaodong He∗ ? Microsoft Research, † University of Washington, ‡ Allen Instit

Source URL: rishabhmit.bitbucket.io

• Machine learning
• Computing
• Data management
• Artificial intelligence
• Computational neuroscience
• Artificial neural networks
• Computational linguistics
• Meta learning
• Supervised learning
• Support vector machine
• Deep learning
• SQL syntax

Meta-Learning as a Collaborative Filtering Problem Mustafa Misir, Mich`ele Sebag MetaSel@ECAI 2014, August 2014

Source URL: www.lri.fr

Learning to learn: meta-learning for medical image segmentation Imagine you have experience with two medical imaging applications (for example, segmentation in brain MR images, and segmentation of cells in histopatholog

Source URL: www.veronikach.com

A Low Time-Consuming Rosters Evaluation in Personnel Scheduling Problems Based on Pattern Learning Roman Vaclavik, Premysl Sucha, Zdenek Hanzalek∗ Abstract Numerous (meta-)heuristics for solving personnel scheduling pr

Source URL: rtime.felk.cvut.cz

Meta-Interpretive Learning: achievements and challenges Stephen Muggleton Department of Computing Imperial College, London

Source URL: 2017.ruleml-rr.org

Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning Yury Ustinovskiy Valentina Fedorova Gleb Gusev Pavel Serdyukov

Source URL: jmlr.org

Child Development, January/February 2011, Volume 82, Number 1, Pages 405–432 The Impact of Enhancing Students’ Social and Emotional Learning: A Meta-Analysis of School-Based Universal Interventions Joseph A. Durlak

Source URL: www.wondergrovelearn.com