Despite their great success in many areas, deep neural networks frequently suffer from poor generalization performance on out-of-domain data. Good inductive biases, i.e. assumptions in the model to learn the target function and to generalize beyond training data, can help overcome this issue. One source of inspiration to look for such inductive biases are brains, as they frequently demonstrate great generalization abilities on a variety of tasks. My goal is to identify methods that allow us to reliably transfer inductive biases from a source environment (e.g. from a robust artificial neural network) to a target environment (e.g. to a previously non-robust network). Once such a method is found, we can use it to transfer inductive biases from biological to artificial neural networks and hopefully gain further insight for both along the way.
Lab members are shown in this color.
2023
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Pawel A. Pierzchlewicz, Konstantin F. Willeke, Arne F. Nix, Pavithra Elumalai, Kelli Restivo, Tori Shinn, Cate Nealley, Gabrielle Rodriguez, Saumil Patel, Katrin Franke, Andreas S. Tolias, Fabian H. Sinz
Energy Guided Diffusion for Generating Neurally Exciting Images
NeurIPS
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Arne Nix, Max Burg, Fabian Sinz
HARD: Hard Augmentations for Robust Distillation
arXiv
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Konstantin F. Willeke1, Kelli Restivo1, Katrin Franke, Arne F. Nix, Santiago A. Cadena, Tori Shinn, Cate Nealley, Gabby Rodriguez, Saumil Patel, Alexander S. Ecker, Fabian H. Sinz2, Andreas S. Tolias2
Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization
bioRxiv
, equal contribution: 1, 2
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2022
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Arne Nix, Max Burg, Fabian Sinz
Leading by example: Guiding knowledge transfer with adversarial data augmentation
Neurips 2022 Workshop SyntheticData4ML
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Arne Nix, Suhas Shrinivasan, Edgar Y. Walker, Fabian H. Sinz
Can Functional Transfer Methods Capture Simple Inductive Biases?
AISTATS 2022
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2021
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Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago A. Cadena, Kelli Restivo, George Denfield, Andreas S. Tolias, Fabian H. Sinz
Towards robust vision by multi-task learning on monkey visual cortex
NeurIPS (accepted)
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Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago A. Cadena, Kelli Restivo, George Denfield, Andreas S. Tolias, Fabian H. Sinz
Towards robust vision by multi-task learning on monkey visual cortex
ICLR 2021 Workshop: How Can Findings About The Brain Improve AI Systems?
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