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How Does Supernet Help in Neural Architecture Search?
With the success of Neural Architecture Search (NAS), weight sharing, as...
10/16/2020 ∙ by Yuge Zhang, et al. ∙
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AutoADR: Automatic Model Design for Ad Relevance
Large-scale pre-trained models have attracted extensive attention in the...
10/14/2020 ∙ by Yiren Chen, et al. ∙
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Interpretable and Efficient Heterogeneous Graph Convolutional Network
Graph Convolutional Network (GCN) has achieved extraordinary success in ...
05/27/2020 ∙ by Yaming Yang, et al. ∙
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LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
BERT is a cutting-edge language representation model pre-trained by a la...
04/08/2020 ∙ by Yihuan Mao, et al. ∙
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Improving BERT with Self-Supervised Attention
One of the most popular paradigms of applying large, pre-trained NLP mod...
04/08/2020 ∙ by Xiaoyu Kou, et al. ∙
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Deeper Insights into Weight Sharing in Neural Architecture Search
With the success of deep neural networks, Neural Architecture Search (NA...
01/06/2020 ∙ by Yuge Zhang, et al. ∙
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TextNAS: A Neural Architecture Search Space tailored for Text Representation
Learning text representation is crucial for text classification and othe...
12/23/2019 ∙ by Yujing Wang, et al. ∙
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An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation
Graph Convolutional Network (GCN) has attracted intensive interests rece...
10/10/2019 ∙ by Nezihe Merve Gürel, et al. ∙
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