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Explores decoding from neural models in modern NLP, covering encoder-decoder models, decoding algorithms, issues with argmax decoding, and the impact of beam size.
Delves into spatial memory usage in RL agents for maze navigation tasks, showing improved performance with visual landmarks but inconsistent results in path choosing.
Explains the full architecture of Transformers and the self-attention mechanism, highlighting the paradigm shift towards using completely pretrained models.
Explores chemical reaction prediction using generative models and molecular transformers, emphasizing the importance of molecular language processing and stereochemistry.
