Mar 12, 2024 · We propose the robustifying and boosting training-free NAS (RoBoT) algorithm which (a) employs the optimized combination of existing training-free metrics.

This repository is the official implementation of Robustifying and Boosting Training-Free Neural Architecture Search. The DARTS space implementation is based on ...

The robustifying and boosting training-free NAS (RoBoT) algorithm is proposed which employs the optimized combination of existing training-free metrics ...

Neural architecture search (NAS) has gained immense popularity owing to its ability to automate neural architecture design. A number of training-free ...

This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in ...

Here we introduce Elektrum, a deep learning framework which addresses these challenges with data-driven and biophysically interpretable models.

This paper proposes GEA, a novel approach for guided NAS. GEA guides the evolution by exploring the search space by generating and evaluating several ...

Our work tackles the problem by automatically designing neural architectures that perform well after adversarial training. 2.2. Neural Architecture Search (NAS).

Neural architecture search (NAS) has become a key component of AutoML and a standard tool to automate the design of deep neural networks. Recently, training ...