59 citations · 140 across the 14 of their papers we have counts for
7 papers · 1 filter
Fast Adversarial Attacks with Gradient Prediction
Kamil Ciosek, Aleksandr V. Petrov, Nicolò Felicioni +1
Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even "fast" attacks such as FGSM remain throughpu…
Mapping Faithful Reasoning in Language Models
Jiazheng Li, Andreas Damianou, J Rosser +2
Chain-of-thought (CoT) traces promise transparency for reasoning language models, but prior work shows they are not always faithful reflections of internal computation. This raises…
A Gaussian Process View on Observation Noise and Initialization in Wide Neural Networks
Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna +4
Performing gradient descent in a wide neural network is equivalent to computing the posterior mean of a Gaussian Process with the Neural Tangent Kernel (NTK-GP), for a specific pri…
Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel
Sergio Calvo-Ordoñez, Konstantina Palla, Kamil Ciosek
Recent work has shown that training wide neural networks with gradient descent is formally equivalent to computing the mean of the posterior distribution in a Gaussian Process (GP)…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla +3
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may deci…
An Infinite Latent Attribute Model for Network Data
Konstantina Palla, David Knowles, Zoubin Ghahramani
Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network in…