research
∙
07/12/2023
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
The computation necessary for training Transformer-based language models...
share
research
∙
01/27/2023
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Likelihood-free inference methods typically make use of a distance betwe...
research
∙
09/15/2022
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and st...
research
∙
11/19/2021
Composite Goodness-of-fit Tests with Kernels
Model misspecification can create significant challenges for the impleme...
research
∙
03/16/2021
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Counterfactual explanations (CEs) are a practical tool for demonstrating...
research
∙
02/22/2021
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression
We propose a new model that estimates uncertainty in a single forward pa...
research
∙
11/01/2020
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
We show that the gradient estimates used in training Deep Gaussian Proce...
research
∙
10/08/2020