Efficient Neural Architecture Search with Network Morphism
By Haifeng Jin, Qingquan Song, Xia Hu. 2018
81 citations recorded by the source corpus.
Subfields: Neural Architecture Search & AutoML
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In Neural Architecture Search & AutoML
Citations in the corpus
- Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, background): “To save the rare resource of experts experience, automated machine learning (AutoML) has been proposed as a burgeoning technology to design and train neural networks automatically, at the cost of computational resources (Feurer et al., 2015; Katz et al., 2016; Bello et al., 2017; Zoph and Le, 2016; ”
- Towards modular and programmable architecture search (2019, background): “Systems such as Auto-Sklearn [19], TPOT [20], and Auto-Keras [21] have been developed for specific use-cases (e.”
- NAS-Unet: Neural Architecture Search for Medical Image Segmentation (2019, methodology): “and not overly expansive search space, even use a random searchmay achieve strong results [20], [40].”
- Language Models with Transformers (2019, background): “mization (Jin et al., 2018) or simple greedy search (Elsken et al.”
- Fast and Practical Neural Architecture Search (2019, methodology): “To ensure block diversity, our proposed FPNAS searches for the whole network instead of a single DAG as in [40, 15, 12].”
- Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, unknown): “, [7], [20], [40], [54], [55], [56], [57], [58], [59], [60], [61], [62], [72]).”
- Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS (2019, background): “Jin et al. (2018) and Weill et al. (2019) presented the most inspiring frameworks for NAS and Auto-ML called Auto Keras and AdaNet respectively.”