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Engineering | ASU Active Perception Group (APG) / Yezhou Yang

Seminar Info:

Host: Chitta Baral and Yezhou Yang

Time: Every Thursday at Noon

Location: BYENG 210

Sponsors:

CCC Post-Doc best practice program;

NVIDIA DLI program.

Topic lists:

Intro to Deep Learning: Yezhou Yang

Activation Functions:
Basic perceptron, Sigmoid, ReLU, (leaky) ReLU, identity, tanh, 
softmax
(Yezhou, Vyom)


Neural network modules:
feed-forward, Word2Vec, CNN (Convolutional Neural Networks), Recurrent Neural Networks (RNNS), RNN with memory units - LSTM,  Encoders (auto encoders), Decoders, Recursive Neural Networks, Generative Models, GANs (Generative Adversarial Networks), Relational Networks, Adversarial networks

(Kausic - CNNs, Chieh-Yang - Word2Vec, Rudra - GAN, Xin - LSTM) 

Modules and Applications: 
Vision - CNN for recognition, GAN (and its extensions such as InfoGANs) 
Language - RNN, LSTM
Decision Making - 

Software and libraries: Tensorflow, Theano, Torch, Keras (a Python library), Caffe,
ONNX (an ecosystem for interchangeable AI frameworks)

Tuning Parameters:
network structure (how many layers), filter size of a CNN,
learning rate (adaptive learning rate algorithms),
how many epochs
(Somak, Rudra, Mo)

Reinforcement Learning using NN
(Rudra - Recap of Reinforcement Learning Workshop)

Recap of EMNLP.

Students talking about their own research.


Training Methods: SGD, …
(ChengXi @UMD - remote presentation)


Important Developments:
AlphaGo, …


Challenges and Questions:
How to do incorporate background knowledge?
How to make inference based on deep learning explainable?
Relational Learning?
Planning