This is a graduate-level research course on deep learning. We will discuss/present the newest and important publications in deep learning, specifically Transformer-based techniques in the areas of knowledge base, question answering, conversational AI, natural language processing and multimodal learning.
Each student is expected to read papers before the lecture, write paper reviews, present papers, and complete a research-quality course project (e.g., implement an existing algorithm or solve a new problem creatively using deep learning; one team could have two students). Projects that simply apply CNN, LSTM or Transformers are not encouraged.
Prerequisites: Neural network building experience or successfully finished an introductory deep learning course before.