Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cognitively Inspired Learning of Incremental Drifting Concepts   Humans continually expand their learned knowledge to new domains and learn
new concepts without any interference with past learned experiences. In
contrast, machine learning models perform poorly in a continual learning
setting, where input data distribution changes over time. Inspired by the
nervous system learning mechanisms, we develop a computational model that
enables a deep neural network to learn new concepts and expand its learned
knowledge to new domains incrementally in a continual learning setting. We rely
on the Parallel Distributed Processing theory to encode abstract concepts in an
embedding space in terms of a multimodal distribution. This embedding space is
modeled by internal data representations in a hidden network layer. We also
leverage the Complementary Learning Systems theory to equip the model with a
memory mechanism to overcome catastrophic forgetting through implementing
pseudo-rehearsal. Our model can generate pseudo-data points for experience
replay and accumulate new experiences to past learned experiences without
causing cross-task interference.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg