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Found 5 papers, 2 papers with code
Transformers in Reinforcement Learning: A Survey
We present a broad overview of how transformers have been adapted for several applications, including robotics, medicine, language modeling, cloud computing, and combinatorial optimization.
Deep Learning and Ethics
This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook. com.
Optimizing Deeper Transformers on Small Datasets
This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.
Normalizing Flows: An Introduction and Review of Current Methods
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact.
Modeling Object Appearance Using Context-Conditioned Component Analysis
In this work we remove the image space alignment limitations of existing subspace models by conditioning the models on a shape dependent context that allows for the complex, non-linear structure of the appearance of the visual object to be captured and shared.
