Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guiding Neural Network Initialization via Marginal Likelihood
  Maximization   We propose a simple, data-driven approach to help guide hyperparameter
selection for neural network initialization. We leverage the relationship
between neural network and Gaussian process models having corresponding
activation and covariance functions to infer the hyperparameter values
desirable for model initialization. Our experiment shows that marginal
likelihood maximization provides recommendations that yield near-optimal
prediction performance on MNIST classification task under experiment
constraints. Furthermore, our empirical results indicate consistency in the
proposed technique, suggesting that computation cost for the procedure could be
significantly reduced with smaller training sets.

25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga