Tag: Deep Learning
Exploring DNN via Layer-Peeled Model
This note is for Fang, C., He, H., Long, Q., & Su, W. J. (2021). Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training. ArXiv:2101.12699 [Cs, Math, Stat].
Fantastic Generalization Measures and Where to Find Them
The post is based on Jiang, Y., Neyshabur, B., Mobahi, H., Krishnan, D., & Bengio, S. (2019). Fantastic Generalization Measures and Where to Find Them. ArXiv:1912.02178 [Cs, Stat].which was shared by one of my friend in the WeChat Moment, and then I took a quick look.
A Optimal Control Approach for Deep Learning
This note is based on Li, Q., & Hao, S. (2018). An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. ArXiv:1803.01299 [Cs].
Medicine Meets AI
Last two days, I attended the conference Medicine Meets AI 2019: East Meets West, which help me know more AI from the industrial and medical perspective.
A Bayesian Perspective of Deep Learning
This note is for Polson, N. G., & Sokolov, V. (2017). Deep Learning: A Bayesian Perspective. Bayesian Analysis, 12(4), 1275–1304.
This note is based on LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.