147 citations · 297 across the 10 of their papers we have counts for
5 papers · 1 filter
Fine-tuning can cripple your foundation model; preserving features may be the solution
Jishnu Mukhoti, Yarin Gal, Philip H. S. Torr +1
Pre-trained foundation models, due to their enormous capacity and exposure to vast amounts of data during pre-training, are known to have learned plenty of real-world concepts. An…
Deep Deterministic Uncertainty: A Simple Baseline
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort +2
Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take…
On Batch Normalisation for Approximate Bayesian Inference
Jishnu Mukhoti, Puneet K. Dokania, Philip H. S. Torr +1
We study batch normalisation in the context of variational inference methods in Bayesian neural networks, such as mean-field or MC Dropout. We show that batch-normalisation does no…
Calibrating Deep Neural Networks using Focal Loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal +3
Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to…
On the Importance of Strong Baselines in Bayesian Deep Learning
Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal
Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals. Given the many aspects of an experiment it is always…