61 citations · 160 across the 21 of their papers we have counts for
4 papers · 1 filter
How to talk so AI will learn: Instructions, descriptions, and autonomy
Theodore R Sumers, Robert D Hawkins, Mark K Ho +2
From the earliest years of our lives, humans use language to express our beliefs and desires. Being able to talk to artificial agents about our preferences would thus fulfill a cen…
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta +7
Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive bi…
Learning Rewards from Linguistic Feedback
Theodore R. Sumers, Mark K. Ho, Robert D. Hawkins +2
We explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive lear…
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs
Andreas Stuhlmüller, Robert X. D. Hawkins, N. Siddharth +1
Many practical techniques for probabilistic inference require a sequence of distributions that interpolate between a tractable distribution and an intractable distribution of inter…