Reinforcement-learning-based neural architecture search finds smaller and faster neural networks with accuracy comparable to, or better than, manually designed networks on three cancer drug-response benchmarks.
Bayesian Learning of Neural Network Architectures
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abstract
In this paper we propose a Bayesian method for estimating architectural parameters of neural networks, namely layer size and network depth. We do this by learning concrete distributions over these parameters. Our results show that regular networks with a learnt structure can generalise better on small datasets, while fully stochastic networks can be more robust to parameter initialisation. The proposed method relies on standard neural variational learning and, unlike randomised architecture search, does not require a retraining of the model, thus keeping the computational overhead at minimum.
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Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Reinforcement-learning-based neural architecture search finds smaller and faster neural networks with accuracy comparable to, or better than, manually designed networks on three cancer drug-response benchmarks.