Search Results for author:
Found 11 papers, 6 papers with code
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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Neural Architecture Search for Dense Prediction Tasks in Computer Vision
The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering.
Bag of Tricks for Neural Architecture Search
While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for being unstable, being highly sensitive with respect to their hyperparameters, and often not performing better than random search.
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural network to use and tuning the hyperparameters of the used training pipeline.
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
On a variety of classification tasks and modern architecture search spaces, we show that the resulting ensembles outperform deep ensembles not only in terms of accuracy but also uncertainty calibration and robustness to dataset shift.
Meta-Learning of Neural Architectures for Few-Shot Learning
The recent progress in neural architecture search (NAS) has allowed scaling the automated design of neural architectures to real-world domains, such as object detection and semantic segmentation.
Automated design of error-resilient and hardware-efficient deep neural networks
It is thus desirable to exploit optimization potential for error resilience and efficiency also at the algorithmic side, e. g., by optimizing the architecture of the DNN.
Understanding and Robustifying Differentiable Architecture Search
Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem.
Neural Architecture Search: A Survey
Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation.
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution
Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts.
Simple And Efficient Architecture Search for Convolutional Neural Networks
Neural networks have recently had a lot of success for many tasks.
