Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weight Agnostic Neural Networks   Not all neural network architectures are created equal, some perform much
better than others for certain tasks. But how important are the weight
parameters of a neural network compared to its architecture? In this work, we
question to what extent neural network architectures alone, without learning
any weight parameters, can encode solutions for a given task. We propose a
search method for neural network architectures that can already perform a task
without any explicit weight training. To evaluate these networks, we populate
the connections with a single shared weight parameter sampled from a uniform
random distribution, and measure the expected performance. We demonstrate that
our method can find minimal neural network architectures that can perform
several reinforcement learning tasks without weight training. On a supervised
learning domain, we find network architectures that achieve much higher than
chance accuracy on MNIST using random weights. Interactive version of this
paper at https://weightagnostic.github.io/

18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org