Do node-based BNNs with latent node variables maintain higher accuracy on CIFAR-10-C than weight-based BNNs wh

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN)

Research goal: Do node-based BNNs with latent node variables maintain higher accuracy on CIFAR-10-C than weight-based BNNs when the number of Monte Carlo samples is reduced to 5?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.3/10.