Weight-Agnostic Neural Networks
&gt;&gt; Our work has connections to existing work not only in deep learning, but also
to various other fields<p>Perhaps I&#x27;m, er, biased, but the most obvious similarity that immediately
jumped out to me when I started reading this article is with Inductive
Programming, the broader field of machine learning algorithms that learn
programs, that includes Inductive Functional Programming and Inductive Logic
Programming (which I study; hence, &quot;biased&quot;).<p>In fact, I&#x27;d go as far as to say that this is a new kind of Inductive
Programming. Perhaps it should be called Inductive Continuous Programming or
Inductive Neural Progamming or some such. But what&#x27;s exceedingly clear to me
is that what the article describes is a search for a <i>program</i> that is composed
of continuous functions, guided by a strong structural bias.<p>The hallmarks of Inductive Progamming include an explicit encoding of strong
structural biases, including by the use of a library of primitives from which
the learned model is composed; a strong Occamist bias; learned models that are
programs. This work ticks all the boxes. The authors might benefit from
looking up the relevan bibliography.<p>A couple of starting pointers:<p>Magic Haskeller, an Inductive Functional Programming system.<p><a href="http:&#x2F;&#x2F;nautilus.cs.miyazaki-u.ac.jp&#x2F;~skata&#x2F;MagicHaskeller.html" rel="nofollow">http:&#x2F;&#x2F;nautilus.cs.miyazaki-u.ac.jp&#x2F;~skata&#x2F;MagicHaskeller....</a><p>Metagol, an Inductive Logic Programming system:<p><a href="https:&#x2F;&#x2F;github.com&#x2F;metagol&#x2F;metagol" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;metagol&#x2F;metagol</a><p>ILASP, a system for the Inductive Learning of Answer Set Programs:<p><a href="http:&#x2F;&#x2F;ilasp.com&#x2F;" rel="nofollow">http:&#x2F;&#x2F;ilasp.com&#x2F;</a>