Sign in

Learning Differentiable Programs with Admissible Neural Heuristics

By Ameesh Shah and others
We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and interpretability; however, learning them requires optimizing over a combinatorial space of program "architectures". We frame this optimization problem as a search in a weighted graph... Show more
July 26, 2020
=
0
Loading PDF…
Loading full text...
Similar articles
Loading recommendations...
=
0
x1
Learning Differentiable Programs with Admissible Neural Heuristics
Click on play to start listening