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About Auto-Pytorch Tabular: Multi-Fidelity Metalearning For Efficient and Robust Autodl
1) The document introduces Auto-PyTorch Tabular, a new AutoML framework that uses multi-fidelity optimization to jointly optimize neural network architectures and hyperparameters for tabular data. 2) It presents a new benchmark, LCBench, to study multi-fidelity optimization approaches for deep learning. LCBench provides learning curves for 2,000 configurations across 35 datasets. 3) Auto-PyTorch Tabular combines multi-fidelity optimization, ensemble learning, and meta-learning techniques. It uses these to robustly search a joint space of architectures and hyperparameters for tabular data and achieves state-of-the-art performance.
Detailed Information
| Author: | ['Mathias Rosas'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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