1 code implementation • 28 Feb 2021 • Jin Chen, Ju Xu, Gangwei Jiang, Tiezheng Ge, Zhiqiang Zhang, Defu Lian, Kai Zheng
However, interactions between creative elements may be more complex than the inner product, and the FM-estimated CTR may be of high variance due to limited feedback.
1 code implementation • 7 Oct 2020 • Ju Xu, Mengzhang Li, Zhanxing Zhu
Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks.
2 code implementations • 30 May 2019 • Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu
Recently, DARTS, which constructs a differentiable search space and then optimizes it by gradient descent, can obtain high-performance architecture and reduces the search time to several days.
no code implementations • 10 May 2019 • Ju Xu, Jin Ma, Zhanxing Zhu
Though neural networks have achieved much progress in various applications, it is still highly challenging for them to learn from a continuous stream of tasks without forgetting.
1 code implementation • NeurIPS 2018 • Ju Xu, Zhanxing Zhu
In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies.