10/5/16

　　Ian Goodfellow: Adversarial Examples and Adversarial Training

　　主要阅读材料：

Explaining and Harnessing Adversarial Examples, by Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples, by Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow

　　背景阅读：

Intriguing properties of neural networks, by Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus

　　Lecture 8

　　10/12/16

　　: Sequences and one-shot learning

　　主要阅读材料：

Matching Networks for One Shot Learning, by Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra

One-shot Learning with Memory-Augmented Neural Networks, by Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap

　　Lecture 9

　　10/19/16

　　Bill Dally: Hardware for Deep Learning

　　主要阅读材料：

EIE: Efficient Inference Engine on Compressed Deep Neural Network, by Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally

Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, by Song Han, Huizi Mao, and William J. Dally

　　背景阅读：

Deep learning with COTS HPC systems, by Adam Coates, Brody Huval, Tao Wang, David J. Wu, Andrew Y. Ng, and Bryan Cantanzaro

　　Lecture 10

　　10/26/16

　　Hugo Larochelle: Autoregressive Generative Models with Deep Learning

　　主要阅读材料：

Neural Autoregressive Distribution Estimation, by Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, and Hugo Larochelle

　　背景阅读：

Deep Learning, Chapter 20 (Generative Models)

Sections 20.1 to 20.4 (background on undirected graphical models)

Sections 20.9 and 20.10.1 to 20.10.5 (background on directed graphical models)

　　Lecture 11

　　11/2/16

　　Lecture 12

　　11/9/16

　　Lecture 13

　　11/16/16

　　Lecture 14

　　11/23/16

　　Lecture 15

　　11/30/16