演講/活動
2019-12-05 16:47:24Patty Chen敬邀參加108/12/17貢三元教授 專題演講～From AI2.0's Deep Learning to XAI's Internal Learning Paradigms～歡迎報名參加～
時間：108年12月17日(週二) 13:20~15:10
地點：國立交通大學工程四館B1合勤講堂
報名網址：https://ppt.cc/frIP7x
地點：國立交通大學工程四館B1合勤講堂
報名網址：https://ppt.cc/frIP7x
因本次為免費研討會活動，竭誠歡迎有興趣的先進報名參加，且務必請大家登入系統報名，以利統計人數。謝謝您。
Abstract:
Deep Learning (AI 2.0) has rapidly become the main stream for machine learning research and development. However, serious technical barriers still effectively prevent its cost-effective deployment to mobile/edge applications. Most notably: the network complexity, power consumption, and long latency. This calls for a critical need of systematical design of high-performing and low-complexity network models. This talk proposes a viable solution using a notion of structural gradient for network structural design.
Pursuant to our simulation finding, X-learning achieves high accuracy with substantially reduced complexity and, by and large, outperforms other state-of-the-art methods, e.g.
(a) it edges the 2018 LPIRC winner when applied to ImageNet classification and (b) outperforms the 2018 PIRM winner in SR Imaging applications.
Abstract:
Deep Learning (AI 2.0) has rapidly become the main stream for machine learning research and development. However, serious technical barriers still effectively prevent its cost-effective deployment to mobile/edge applications. Most notably: the network complexity, power consumption, and long latency. This calls for a critical need of systematical design of high-performing and low-complexity network models. This talk proposes a viable solution using a notion of structural gradient for network structural design.
Pursuant to our simulation finding, X-learning achieves high accuracy with substantially reduced complexity and, by and large, outperforms other state-of-the-art methods, e.g.
(a) it edges the 2018 LPIRC winner when applied to ImageNet classification and (b) outperforms the 2018 PIRM winner in SR Imaging applications.
