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To improve one-shot NAS, we apply curriculum learning to the sharing extent in one-shot supernet to achieve better ranking correlation.

We study one-shot performance estimators and 8 types of zero-shot estimators on 5 different benchmarks (NB101, 201, 301, NDS ResNet, ResNeXt-A).

TA-GATES is an encoding scheme specially designed for neural architectures, considering their distinguishing properties as DAGs with trainable operations. TA-GATES encodes an architecture by mimicking its training process, and thereby provides more discriminative architecture-level and operation-level encodings.

In order to improve the sample efficiency of NAS, we follow the line of predictor-based NAS and improve the encoder design and training of the predictor. (1) We design a Generic Graph-based neural ArchiTecture Encoding Scheme (GATES) to better encode NN architectures. (2) We propose to use ranking loss to train the predictor.

To mitigate the cold-start problem of predictor-based NAS, we design an ensemble method to fuse the knowledge from multiple experts trained with low-fidelity architectural information (e.g., complexities, zero-shot metrics).

We apply MARL to guide traffic participants to construct valuable hazardous scenarios for under-test policies. We run the proposed VDARS method on Baidu Apollo and other types of driving policies (including learning-based and rule-based ones) and discover diverse AV-responsible hazardous scenarios valuable for further improvements of autonomous driving policies.