Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence Analysis and Implicit Regularization of Feedback Alignment
  for Deep Linear Networks   We theoretically analyze the Feedback Alignment (FA) algorithm, an efficient
alternative to backpropagation for training neural networks. We provide
convergence guarantees with rates for deep linear networks for both continuous
and discrete dynamics. Additionally, we study incremental learning phenomena
for shallow linear networks. Interestingly, certain specific initializations
imply that negligible components are learned before the principal ones, thus
potentially negatively affecting the effectiveness of such a learning
algorithm; a phenomenon we classify as implicit anti-regularization. We also
provide initialization schemes where the components of the problem are
approximately learned by decreasing order of importance, thus providing a form
of implicit regularization.

36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph