41 citations · 103 across the 5 of their papers we have counts for
5 papers
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
Kevin Ellis, Catherine Wong, Maxwell Nye +6
Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, a…
Amanuensis: The Programmer's Apprentice
Thomas Dean, Maurice Chiang, Marcus Gomez +9
This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon insight from cognitive and systems ne…
Transfer Learning with Neural AutoML
Catherine Wong, Neil Houlsby, Yifeng Lu +1
We reduce the computational cost of Neural AutoML with transfer learning. AutoML relieves human effort by automating the design of ML algorithms. Neural AutoML has become popular f…
DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation
Catherine Wong
Machine learning models are powerful but fallible. Generating adversarial examples - inputs deliberately crafted to cause model misclassification or other errors - can yield import…
Transfer Learning to Learn with Multitask Neural Model Search
Catherine Wong, Andrea Gesmundo
Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task. The exploration of the model design space is…