Search Results for author:
Found 6 papers, 3 papers with code
The UniNAS framework: combining modules in arbitrarily complex configurations with argument trees
A configuration file specifies which modules are used, their used parameters, and which other modules they use in turn This concept of argument trees enables combining and reusing modules in complex configurations while avoiding many problems mentioned above.
What to expect of hardware metric predictors in NAS
Due to inaccurate predictions, the selected architectures are generally suboptimal, which we quantify as an expected reduction in accuracy and hypervolume.
Inter-choice dependent super-network weights
The automatic design of architectures for neural networks, Neural Architecture Search, has gained a lot of attention over the recent years, as the thereby created networks repeatedly broke state-of-the-art results for several disciplines.
Exploring single-path Architecture Search ranking correlations
Recently presented benchmarks for Neural Architecture Search (NAS) provide the results of training thousands of different architectures in a specific search space, thus enabling the fair and rapid comparison of different methods.
Prune and Replace NAS
While recent NAS algorithms are thousands of times faster than the pioneering works, it is often overlooked that they use fewer candidate operations, resulting in a significantly smaller search space.
ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search
Neural network architectures found by sophistic search algorithms achieve strikingly good test performance, surpassing most human-crafted network models by significant margins.
