Explores pretraining sequence-to-sequence models with BART and T5, discussing transfer learning, fine-tuning, model architectures, tasks, performance comparison, summarization results, and references.
Explores chemical reaction prediction using generative models and molecular transformers, emphasizing the importance of molecular language processing and stereochemistry.
Explains the full architecture of Transformers and the self-attention mechanism, highlighting the paradigm shift towards using completely pretrained models.
