Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Light-in-the-loop: using a photonics co-processor for scalable training
  of neural networks   As neural networks grow larger and more complex and data-hungry, training
costs are skyrocketing. Especially when lifelong learning is necessary, such as
in recommender systems or self-driving cars, this might soon become
unsustainable. In this study, we present the first optical co-processor able to
accelerate the training phase of digitally-implemented neural networks. We rely
on direct feedback alignment as an alternative to backpropagation, and perform
the error projection step optically. Leveraging the optical random projections
delivered by our co-processor, we demonstrate its use to train a neural network
for handwritten digits recognition.

18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
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
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an