Sophia Sanborn — accepted AI-conference papers

2026

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Konstantin Friedrich Willeke, Polina Turishcheva, Alex Gilbert, Goirik Chakrabarty, Hasan Atakan Bedel, Paul G. Fahey +15

ICLR 2026poster

Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.3 million neurons from the visual cortex of 78 mice across 323 sessions, totali…

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2024

The Selective $G$-Bispectrum and its Inversion: Applications to $G$-Invariant Networks

Simon Mataigne, Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane

NeurIPS 2024poster

An important problem in signal processing and deep learning is to achieve *invariance* to nuisance factors not relevant for the task. Since many of these factors are describable as the action of a group $G$ (e.g. rotations, translations, scalings), we want methods to be $G$-invariant. The $G$-Bispec…

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2023

A General Framework for Robust G-Invariance in G-Equivariant Networks

Sophia Sanborn, Nina Miolane

NeurIPS 2023poster

We introduce a general method for achieving robust group-invariance in group-equivariant convolutional neural networks ($G$-CNNs), which we call the $G$-triple-correlation ($G$-TC) layer. The approach leverages the theory of the triple-correlation on groups, which is the unique, lowest-degree polyno…

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2023

Bispectral Neural Networks

Sophia Sanborn, Christian A Shewmake, Bruno Olshausen, Christopher J. Hillar

ICLR 2023poster

We present a neural network architecture, Bispectral Neural Networks (BNNs) for learning representations that are invariant to the actions of compact commutative groups on the space over which a signal is defined. The model incorporates the ansatz of the bispectrum, an analytically defined group inv…

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