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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…
Continual Learning in Low-rank Orthogonal Subspaces
Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania +1
In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experie…
Diagnosing and Preventing Instabilities in Recurrent Video Processing
Thomas Tanay, Aivar Sootla, Matteo Maggioni +4
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution. In this work, we focus on their stability as dynamical systems and sh…
How benign is benign overfitting?
Amartya Sanyal, Puneet K Dokania, Varun Kanade +1
We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models. When trained with SGD, deep neural networks essentially achie…
Progressive Skeletonization: Trimming more fat from a network at initialization
Pau de Jorge, Amartya Sanyal, Harkirat S. Behl +3
Recent studies have shown that skeletonization (pruning parameters) of networks \textit{at initialization} provides all the practical benefits of sparsity both at inference and tra…
Simulation-Based Inference for Global Health Decisions
Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli +8
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision m…
A Revised Generative Evaluation of Visual Dialogue
Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania +2
Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge. The current evaluation scheme of the VisDi…
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…
Using Hindsight to Anchor Past Knowledge in Continual Learning
Arslan Chaudhry, Albert Gordo, Puneet K. Dokania +2
In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forge…