Machine learning approaches have recently been applied to the study of various problems in physics. Most of these studies are focused on interpreting the data generated by conventional numerical methods or the data on an existing experimental database. An interesting question is whether it is possible to use a machine learning approach, in particular a neural network, for solving the many-body problem. In this paper, we present a neural network solver for the single impurity Anderson model, the paradigm of an interacting quantum problem in small clusters. We demonstrate that the neural-network-based solver provides quantitative accurate results for the spectral function as compared to the exact diagonalization method. This opens the possibility of utilizing the neural network approach as an impurity solver for other many-body numerical approaches, such as the dynamical mean field theory.
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OVERCOMING CATASTROPHIC FORGETTING FOR CONTINUAL LEARNING VIA MODEL ADAPTATION
Learning multiple tasks sequentially is important for the development of AI and
lifelong learning systems. However, standard neural network architectures suffer
from catastrophic forgetting which makes it difficult for them to learn a sequence
of tasks. Several continual learning methods have been proposed to address the
problem. In this paper, we propose a very different approach, called Parameter
Generation and Model Adaptation (PGMA), to dealing with the problem. The
proposed approach learns to build a model, called the solver, with two sets of
parameters. The first set is shared by all tasks learned so far and the second set
is dynamically generated to adapt the solver to suit each test example in order
to classify it. Extensive experiments have been carried out to demonstrate the
effectiveness of the proposed approach.
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- Award ID(s):
- 1838770
- PAR ID:
- 10120468
- Date Published:
- Journal Name:
- International Conference on Learning Representations
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
