One-shot learning of generative speech concepts Brenden M. Lake* Chia-ying Lee* James R. Glass

Source URL: cims.nyu.edu

• Artificial intelligence
• Learning
• Machine learning
• Computational linguistics
• Markov models
• Hierarchical hidden Markov model
• Bayesian statistics
• Speech recognition
• Hidden Markov model
• Dynamic time warping
• Language model
• Speech perception

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

Proceedings of Machine Learning Research, 4th International Conference on Predictive Applications and APIs An Architecture and Domain Specific Language Framework for Repeated Domain-Specific Predictive M

Source URL: proceedings.mlr.press

• Software engineering
• Computing
• Modeling and simulation
• Software architecture
• Software design
• Domain-specific language
• Domain-driven design
• Conceptual model
• Architectural pattern
• Scientific modelling
• Domain-specific modeling
• Feature engineering

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

www.sciencemag.org/contentsuppl/DC1 Supplementary Material for Human-level concept learning through probabilistic program induction Brenden M. Lake,* Ruslan Salakhutdinov, Joshua B. Tenenbaum *Correspondi

Source URL: cims.nyu.edu

• Interpolation
• Physics
• Spline
• B-spline
• Work

Concept learning as motor program induction: A large-scale empirical study Brenden M. Lake Ruslan Salakhutdinov Joshua B. Tenenbaum

Source URL: cims.nyu.edu

• Metaphysics
• Chinese characters
• Languages of East Asia
• Linguistics
• Calligraphy
• Korean language
• Ordering
• Orthography
• Stroke order
• Stroke
• Concept learning
• Drawer

Thank you for downloading Using Response to Intervention Data to Advance Learning Outcomes Amanda M. VanDerHeyden

Source URL: www.centeril.org