Explores chemical reaction prediction using generative models and molecular transformers, emphasizing the importance of molecular language processing and stereochemistry.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Explores coreference resolution models, challenges in scoring spans, graph refinement techniques, state-of-the-art results, and the impact of pretrained Transformers.
Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.
