Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.
Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.
Covers syntactic structure, dependency parsing, and neural network transition-based parsing, highlighting the importance of dependency structure in linguistic analysis.