Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

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Information Extraction & Knowledge Inference

Explores information extraction, knowledge inference, taxonomy induction, and entity disambiguation.

Deep Learning for NLP

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Compositional Representations and Systematic Generalization

Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

Deep Learning: Graphs and Transformers Overview

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Model Analysis

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

Text Understanding

Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Deep Learning for NLP

Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.