Current Advances in Deep and Recurrent Neural Networks

Dozent	Dr. Sebastian Otte
Zeit	Fr 10 c. t. bis 12 Uhr Seminar
Umfang	3 LP
Beginn	20.04.2017
Ort	Informatik, Sand 14, Poolraum, C 412
Prüfungsfach
Modul

Topics

Very Deep Networks
Densely Connected Convolutional Networks
Generative Adversarial Networks I
Generative Adversarial Networks II: Wasserstein GAN
Generative Adversarial Networks II: Synthesizing Audio
Generative Adversarial Networks II: Image-to-Image Translation
Single Shot MultiBox Detector
Capsule Networks
Recurrent Weighted Average Networks
Recurrent Highway Networks
Nested LSTMs
Optimization and Regularization: SGD vs. Adam and Co.
Optimization and Regularization: Recurrent Batch Normalization
Optimization and Regularization: Batch Kalman Normalization
Spherical CNNs
Variational Autoencoders I
Variational Autoencoders II: Recurrent VAs for music
Wasserstein Auto-Encoders
Bayesian Deep Learning
Uncertainty-Aware Deep Learning
Efficient Deep Learning: ShuffleNet
Efficient Deep Learning: Multi-Scale Dense Networks
Efficient Deep Learning: Integers in Deep Networks
Fooling Deep Networks
Understanding Deep Models
Ethics in Artificial Intelligence and Deep Learning