Transformers: Overview and Self-Attention

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Sequence to Sequence Models: Overview and Applications

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Graph-to-Graph Transformers: Syntax-aware Graph Encoding

Introduces the Syntax-aware Graph-to-Graph Transformer architecture for effective conditioning on syntactic dependency graphs.

BERT: Pretraining and Applications

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

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.

Transformer Architecture: The X Gomega

Delves into the Transformer architecture, self-attention, and training strategies for machine translation and image recognition.

Transformers: Revolutionizing Attention Mechanisms in NLP

Covers the development of transformers and their impact on attention mechanisms in NLP.

Modern NLP and Ethics in NLP

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Chemical Reaction Prediction: Molecular Transformer

Explores chemical reaction prediction using generative models and molecular transformers, emphasizing the importance of molecular language processing and stereochemistry.

Transformers: Full Architecture and Self-Attention Mechanism

Explains the full architecture of Transformers and the self-attention mechanism, highlighting the paradigm shift towards using completely pretrained models.