Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing
  its Gradient Estimator Bias   Equilibrium Propagation (EP) is a biologically-inspired counterpart of
Backpropagation Through Time (BPTT) which, owing to its strong theoretical
guarantees and the locality in space of its learning rule, fosters the design
of energy-efficient hardware dedicated to learning. In practice, however, EP
does not scale to visual tasks harder than MNIST. In this work, we show that a
bias in the gradient estimate of EP, inherent in the use of finite nudging, is
responsible for this phenomenon and that cancelling it allows training deep
ConvNets by EP, including architectures with distinct forward and backward
connections. These results highlight EP as a scalable approach to compute error
gradients in deep neural networks, thereby motivating its hardware
implementation.

17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin