Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Neural Architecture Search (NAS) is quickly becoming the standard methodology to design
neural network models. However, NAS is typically compute-intensive because multiple …

Abstract Machine Learning (ML) has revolutionized various fields, enabling the development
of intelligent systems capable of solving complex problems. However, the process of …

Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique
aiming to significantly speed up algorithms for neural architecture search (NAS). Recent …

Differentiable neural architecture search delivers point estimation to the optimal architecture,
which yields arbitrarily high confidence to the learned architecture. This approach thus …

Conducting efficient performance estimations of neural architectures is a major challenge in
neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot …

Abstract Neural Architecture Search (NAS) is a promising paradigm in automatic architecture
engineering. Zero-shot NAS can evaluate the network without training via some specific …

We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection
models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU …

Abstract Predictor-based Neural Architecture Search (NAS) continues to be an important
topic because it aims to mitigate the time-consuming search procedure of traditional NAS …

Deep neural networks have demonstrated impressive results in medical image analysis, but
designing suitable architectures for each specific task is expertise dependent and time …