Deep Learning in Language and Vision Seminar

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