Search Results for author:
Found 6 papers, 2 papers with code
SuperCoder: Program Learning Under Noisy Conditions From Superposition of States
We propose a new method of program learning in a Domain Specific Language (DSL) which is based on gradient descent with no direct search.
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS).
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS).
Neural Architecture Search: Insights from 1000 Papers
Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas.
Natural Language Understanding
Neural Architecture Search
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Weight-Entanglement Meets Gradient-Based Neural Architecture Search
%Due to the inherent differences in the structure of these search spaces, these Since weight-entanglement poses compatibility challenges for gradient-based NAS methods, these two paradigms have largely developed independently in parallel sub-communities.
Surprisingly Strong Performance Prediction with Neural Graph Features
Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training.
