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![1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
AdaShare: LearningWhatTo Share For Efficient
Deep Multi-Task Learni...](https://image.slidesharecdn.com/dl1211-191213002847/75/DL-AdaShare-Learning-What-To-Share-For-Efficient-Deep-Multi-Task-Learning-1-2048.jpg?cb=1667497405)
![3. 提案手法
7
AdaShareの学習の概要
• 学習されたタスク固有のポリシー分布からサンプリングされた[Skip or Select] を予測することにより、複数のタスク間の重み
共有パターンを学習する。
• 各層は、2つのタスクで使用...](https://image.slidesharecdn.com/dl1211-191213002847/75/DL-AdaShare-Learning-What-To-Share-For-Efficient-Deep-Multi-Task-Learning-7-2048.jpg?cb=1667497405)
![3. 提案手法
8
アプローチ
：ポリシー [0, 1]（Skip or Select）𝑢𝑙,𝑘
：タスク {𝑇1, 𝑇2,・・・ , 𝑇𝑘}𝑇𝑘
𝑙 ：レイヤーの数
𝑊 ：重み
ポリシー𝑈とネットワークの重み𝑊を共に学習させる。
• ポリシー...](https://image.slidesharecdn.com/dl1211-191213002847/75/DL-AdaShare-Learning-What-To-Share-For-Efficient-Deep-Multi-Task-Learning-8-2048.jpg?cb=1667497405)
![Appendix
参考文献
22
• [4] Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In
Proceed...](https://image.slidesharecdn.com/dl1211-191213002847/75/DL-AdaShare-Learning-What-To-Share-For-Efficient-Deep-Multi-Task-Learning-22-2048.jpg?cb=1667497405)