73 citations · 316 across the 15 of their papers we have counts for
10 papers · 1 filter
Prediction-Oriented Bayesian Active Learning
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar +3
Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BA…
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick +1
We investigate the benefit of treating all the parameters in a Bayesian neural network stochastically and find compelling theoretical and empirical evidence that this standard cons…
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
Sören Mindermann, Jan Brauner, Muhammed Razzak +8
Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training,…
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf +4
Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for t…
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation
Jannik Kossen, Sebastian Farquhar, Yarin Gal +1
We propose Active Surrogate Estimators (ASEs), a new method for label-efficient model evaluation. Evaluating model performance is a challenging and important problem when labels ar…
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)
Sören Mindermann, Muhammed Razzak, Winnie Xu +7
We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are "just right". We propose an information-theoretic acqu…