Search Results for author:
Found 9 papers, 5 papers with code
Identifying concept libraries from language about object structure
Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations.
Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks
We first contribute a new challenge benchmark for comparing humans and distributional large language models (LLMs).
Leveraging Language to Learn Program Abstractions and Search Heuristics
Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learning systems.
Communicating Natural Programs to Humans and Machines
We present LARC, the \textit{Language-complete ARC}: a collection of natural language descriptions by a group of human participants who instruct each other on how to solve ARC tasks using language alone, which contains successful instructions for 88\% of the ARC tasks.
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.
Amanuensis: The Programmer's Apprentice
This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018.
Transfer Learning with Neural AutoML
We extend RL-based architecture search methods to support parallel training on multiple tasks and then transfer the search strategy to new tasks.
DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation
In this work, we introduce DANCin SEQ2SEQ, a GAN-inspired algorithm for adversarial text example generation targeting largely black-box text classifiers.
Transfer Learning to Learn with Multitask Neural Model Search
We demonstrate that MNMS can conduct an automated architecture search for multiple tasks simultaneously while still learning well-performing, specialized models for each task.
