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Exploring the Space of Key-Value-Query Models with Intention | Marta Garnelo

ICARL Seminar Series - 2024 Spring Exploring the Space of Key-Value-Query Models with Intention Seminar by Marta Garnelo —————————————————— Abstract: Attention-based models have been a key element of many recent breakthroughs in deep learning. Two key components of Attention are the structure of its input (which consists of keys, values and queries) and the computations by which these three are combined. In this paper we explore the space of models that share said input structure but are not restricted to the computations of Attention. We refer to this space as Keys-Values-Queries (KVQ) Space. Our goal is to determine whether there are any other stackable models in KVQ Space that Attention cannot efficiently approximate, which we can implement with our current deep learning toolbox and that solve problems that are interesting to the community. Maybe surprisingly, the solution to the standard least squares problem satisfies these properties. A neural network module that is able to compute this solution not only enriches the set of computations that a neural network can represent but is also provably a strict generalisation of Linear Attention. Even more surprisingly the computational complexity of this module is exactly the same as that of Attention, making it a suitable drop in replacement. With this novel connection between classical machine learning (least squares) and modern deep learning (Attention) established we justify a variation of our model which generalises regular Attention in the same way. Both new modules are put to the test an a wide spectrum of tasks ranging from few-shot learning to policy distillation that confirm their real-worlds applicability. —————————————————— About the Speaker: Marta is a senior research scientist at DeepMind where she has primarily worked on deep generative models and meta learning. As part of this research she has been involved in developing Generative Query Networks and led the work on Neural Processes. In addition to generative models her recent interests have expanded to multi-agent systems and game theory. Prior to DeepMind Marta obtained her PhD from Imperial College London. —————————————————— Links Marta Garnelo Site: martagarnelo.com Twitter: x.com/GarneloMarta ICARL Site: icarl.doc.ic.ac.uk Twitter: x.com/ic_arl YouTube: @ICARLSeminars ——————————————————

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