Ettore Randazzo

Software engineer researching fundamental problems about artificial intelligence and life.
Authored Publications
Google Publications
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    Preview abstract Transformers have become the state-of-the-art neural network architecture across numerous domains of machine learning. This is partly due to their celebrated ability to transfer and to learn in-context based on a few examples. Nevertheless, the mechanism of why and how Transformers become in-context learners is not well understood and remains mostly an intuition. Here, we argue that training Transformers on auto-regressive tasks can be closely related to well-known gradient-based meta-learning formulations. We do so by providing a simple construction that shows the equivalence of data transformations induced by 1) a single linear self-attention layer and by 2) gradient-descent on a regression loss. Motivated by that construction, we show empirically that when training self-attention only Transformers on simple regression tasks either the models learned by GD and Transformers show great similarity or, remarkably, the solutions found by gradient descent converge in weight space to our construction. This allows us, at least on our simple regression tasks, to mechanistically understand the inner workings of Transformers that enables in-context learning within. Finally, we discuss intriguing parallels to a mechanism identified as crucial for in-context learning termed induction-head (Olsson et al., 2022) and show how it could be generalized by in-context learning by gradient descent within Transformers. View details
    Preview abstract We present a Message Passing based Learning Protocol (MPLP) for artificial neural networks. With this protocol, every synapse (weights and biases), and activation is considered an independent agent, responsible for ingesting incoming messages, updating their own states, and outputting n-dimensional messages for their neighbours. We show how this protocol can be used instead of a traditional gradient-based approach for traditional feed-forward neural networks, and present a framework capable of generalizing neural networks to explore more flexible architectures. We meta-learn the MPLP through end-to-end gradient-based meta-optimisation. Finally, we discuss where the strengths of MPLP lay, and where we foresee possible limitations. View details
    Preview abstract Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns. View details
    Self-classifying MNIST digit CA
    Alexander Mordvintsev {{ +moralex }
    Michael Levin
    Sam Greydanus
    Preview abstract Training an end-to-end differentiable, self-organising cellular automata model able to self-classify in ever-changing MNIST digits. View details
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