The paper compresses the context. This method is called "test time training layer (-)". The layer directly replaces the attention mechanism and unlocks the linear complexity architecture with expressive memory, allowing us to train millions (possibly billions in the future) of k in context. The author believes that this project, which has been studied for more than a year, will fundamentally change our approach to language models. The ability model and learning improvement of end product managers The first major challenge facing end product managers is how to correctly analyze and diagnose business problems. This is also the most difficult part.
Product design knowledge is basically of no list of indian phone numbers help in this part of the work. If you want to do a good job of business analysis and diagnosis, you must have a solid... View details> And the results prove that - and - directly surpassed or defeated the strongest and! One of the authors said with surprise: I can't believe we really did it. What's even more exciting is that although it is currently only used for language modeling, it can also be used for long videos in the future. In the future, when we model long videos, we can densely sample frames instead of sampling.
These dense frame pairs are a burden, but for the layers, this is a blessing! An idea that has been around for more than a year has finally come true. The author said that in the past . years, the team has been developing a new architecture that can have linear complexity and stronger hidden states for long context modeling. And this idea of training during testing has been studied for more than . years. I clearly remember that when I first started as a postdoc, I asked myself to discuss it. This meeting was the starting point of this research.