Abstract
Designing the right neural network architecture for a given machine-learning task is critical for performance. For example, the most appropriate neural networks for tasks such as image classification, speech recognition, click-through-rate prediction, etc. are different from each other. This disclosure describes a framework for conducting searches for neural architectures that perform recommendation and ranking tasks.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Automatic Design of Neural Architectures for Recommendation and Ranking Tasks", Technical Disclosure Commons, (November 04, 2019)
https://www.tdcommons.org/dpubs_series/2646
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