Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accretionary Learning with Deep Neural Networks   One of the fundamental limitations of Deep Neural Networks (DNN) is its
inability to acquire and accumulate new cognitive capabilities. When some new
data appears, such as new object classes that are not in the prescribed set of
objects being recognized, a conventional DNN would not be able to recognize
them due to the fundamental formulation that it takes. The current solution is
typically to re-design and re-learn the entire network, perhaps with a new
configuration, from a newly expanded dataset to accommodate new knowledge. This
process is quite different from that of a human learner. In this paper, we
propose a new learning method named Accretionary Learning (AL) to emulate human
learning, in that the set of objects to be recognized may not be pre-specified.
The corresponding learning structure is modularized, which can dynamically
expand to register and use new knowledge. During accretionary learning, the
learning process does not require the system to be totally re-designed and
re-trained as the set of objects grows in size. The proposed DNN structure does
not forget previous knowledge when learning to recognize new data classes. We
show that the new structure and the design methodology lead to a system that
can grow to cope with increased cognitive complexity while providing stable and
superior overall performance.

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The categories of this paper are: hep-ph
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th