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
