Topic: Adversarial robustness

Four-month, full-time fellowship for empirical AI safety and security research, with researcher mentorship, a weekly stipend and research support. Workstreams include model interpretability, scalable oversight, adversarial robustness and evaluation of advanced AI systems. Applications for the January cohort are open and reviewed on a rolling basis. Shared workspaces are available in Berkeley and London, with remote participation possible in the United States, United Kingdom or Canada. A subsequent permanent role is not guaranteed.

The internal activations of particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses along the ventral visual cortex in primates. Nevertheless, the similarities between the two are often investigated through stimulus sets consisting of everyday objects under naturalistic settings. Recent work has revealed a gap in generalization ability of these models in predicting neuronal responses to out-of-distribution (OOD) samples (i.e. samples that are not regarded as natural photos) . Here, we investigated how the recent progress in improving DNNs’ object re

A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of single neurons and networks of such neurons. Answering this question requires theoretical advances that shine light into the ‘black box’ of neuronal networks. In this talk, I will demonstrate theoretical approaches that help describe how cognitive and behavioral task implementations emerge from structure in neural populations and from biologically plausible learning rules. First, I will introduce an analytic theory that connects geometric structures that arise from neural res