IQNAS: Interpretable Integer Quadratic Programming Neural Architecture
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- URL: http://arxiv.org/abs/2110.12399v1
- Date: Sun, 24 Oct 2021 09:45:00 GMT
- Title: IQNAS: Interpretable Integer Quadratic Programming Neural Architecture
Search
- Authors: Niv Nayman, Yonathan Aflalo, Asaf Noy, Rong Jin, Lihi Zelnik-Manor
- Abstract summary: A popular approach to find fitting networks is through constrained Neural Architecture Search (NAS)
Previous methods use complicated predictors for the accuracy of the network.
We introduce Interpretable Quadratic programming Neural Architecture Search (IQNAS)
- Score: 40.77061519007659
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Realistic use of neural networks often requires adhering to multiple
constraints on latency, energy and memory among others. A popular approach to
find fitting networks is through constrained Neural Architecture Search (NAS).
However, previous methods use complicated predictors for the accuracy of the
network. Those predictors are hard to interpret and sensitive to many
hyperparameters to be tuned, hence, the resulting accuracy of the generated
models is often harmed. In this work we resolve this by introducing
Interpretable Integer Quadratic programming Neural Architecture Search (IQNAS),
that is based on an accurate and simple quadratic formulation of both the
accuracy predictor and the expected resource requirement, together with a
scalable search method with theoretical guarantees. The simplicity of our
proposed predictor together with the intuitive way it is constructed bring
interpretability through many insights about the contribution of different
design choices. For example, we find that in the examined search space, adding
depth and width is more effective at deeper stages of the network and at the
beginning of each resolution stage. Our experiments show that IQNAS generates
comparable to or better architectures than other state-of-the-art NAS methods
within a reduced search cost for each additional generated network, while
strictly satisfying the resource constraints.
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