Deep Learning is often criticized as being a black-box method that provides accurate predictions, but a limited explanation of the underlying processes and no indication when to not trust those ...predictions. Equipping existing deep learning models with an (general) notion of uncertainty can help mitigate both these issues. The Bayesian deep learning community has developed model-agnostic methodology to estimate both data and model uncertainty that can be implemented on top of existing deep learning models. In this work, we test this methodology for deep recurrent satellite time series forecasting and test its assumptions on data and model uncertainty. We tested its effectiveness on an application on climate change where the activity of seasonal vegetation decreased over multiple years.
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 not affect the ...optimum of the evidence lower bound
(ELBO). Furthermore, we study the Monte Carlo Batch Normalisation (MCBN)
algorithm, proposed as an approximate inference technique parallel to MC
Dropout, and show that for larger batch sizes, MCBN fails to capture epistemic
uncertainty. Finally, we provide insights into what is required to fix this
failure, namely having to view the mini-batch size as a variational parameter
in MCBN. We comment on the asymptotics of the ELBO with respect to this
variational parameter, showing that as dataset size increases towards infinity,
the batch-size must increase towards infinity as well for MCBN to be a valid
approximate inference technique.
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task ...of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at: https://github.com/RagMeh11/QU-BraTS.
Multi-agent learning is a promising method to simulate aggregate competitive behaviour in finance. Learning expert agents' reward functions through their external demonstrations is hence particularly ...relevant for subsequent design of realistic agent-based simulations. Inverse Reinforcement Learning (IRL) aims at acquiring such reward functions through inference, allowing to generalize the resulting policy to states not observed in the past. This paper investigates whether IRL can infer such rewards from agents within real financial stochastic environments: limit order books (LOB). We introduce a simple one-level LOB, where the interactions of a number of stochastic agents and an expert trading agent are modelled as a Markov decision process. We consider two cases for the expert's reward: either a simple linear function of state features; or a complex, more realistic non-linear function. Given the expert agent's demonstrations, we attempt to discover their strategy by modelling their latent reward function using linear and Gaussian process (GP) regressors from previous literature, and our own approach through Bayesian neural networks (BNN). While the three methods can learn the linear case, only the GP-based and our proposed BNN methods are able to discover the non-linear reward case. Our BNN IRL algorithm outperforms the other two approaches as the number of samples increases. These results illustrate that complex behaviours, induced by non-linear reward functions amid agent-based stochastic scenarios, can be deduced through inference, encouraging the use of inverse reinforcement learning for opponent-modelling in multi-agent systems.
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