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Found 12 papers, 5 papers with code
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.
Are Vision Language Models Texture or Shape Biased and Can We Steer Them?
If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments.
An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness
We are interested in the single prediction task for robustness and the joint multi-objective of clean and robust accuracy.
Neural Architecture Design and Robustness: A Dataset
We evaluate all these networks on a range of common adversarial attacks and corruption types and introduce a database on neural architecture design and robustness evaluations.
Learning Where To Look -- Generative NAS is Surprisingly Efficient
the optimization of architectures for highest classification accuracy but also in the context of hardware constraints and outperform state-of-the-art methods on several NAS benchmarks for single and multiple objectives.
DARTS for Inverse Problems: a Study on Stability
Differentiable architecture search (DARTS) is a widely researched tool for neural architecture search, due to its promising results for image classification.
Is Differentiable Architecture Search truly a One-Shot Method?
In this work, we investigate DAS in a systematic case study of inverse problems, which allows us to analyze these potential benefits in a controlled manner.
Neural Architecture Performance Prediction Using Graph Neural Networks
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest.
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
We evaluate the proposed approach on neural architectures defined by the ENAS approach, the NAS-Bench-101 and the NAS-Bench-201 search space and show that our smooth embedding space allows to directly extrapolate the performance prediction to architectures outside the seen domain (e. g. with more operations).
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
We show that surrogate NAS benchmarks can model the true performance of architectures better than tabular benchmarks (at a small fraction of the cost), that they lead to faithful estimates of how well different NAS methods work on the original non-surrogate benchmark, and that they can generate new scientific insight.
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest.
Massively Parallel Benders Decomposition for Correlation Clustering
We tackle the problem of graph partitioning for image segmentation using correlation clustering (CC), which we treat as an integer linear program (ILP).
