553 citations · 1.4k across the 41 of their papers we have counts for
5 papers · 1 filter
Dual-Primal Graph VAEs for Noisy Label Aggregation
Patrick Stinson, Nikolaus Kriegeskorte
Inferring the ground-truth from noisy crowdsourced labels is an important theoretical and practical problem. Neural network-based methods offer an alternative to classical Bayesian…
Comparing Linear Probes with Mahalanobis Cosine Similarity
Zhuofan Josh Ying, Peter Hase, Nikolaus Kriegeskorte
Linear probes are widely used in interpretability research and often compared by cosine similarity. The Mahalanobis cosine similarity (MCS) between two directions, which reweights…
Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
Mu Nan, Muquan Yu, Weijian Mai +12
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computationa…
The Truthfulness Spectrum Hypothesis
Zhuofan Josh Ying, Shauli Ravfogel, Nikolaus Kriegeskorte +1
Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding's generality. We reconcile these views with the truthfulness…
Robustly representing uncertainty in deep neural networks through sampling
Patrick McClure, Nikolaus Kriegeskorte
As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the ke…