42 research outputs found
Context-Aware Generative Models for Prediction of Aircraft Ground Tracks
Trajectory prediction (TP) plays an important role in supporting the
decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are
deterministic and physics-based, with parameters that are calibrated using
aircraft surveillance data harvested across the world. These models are,
therefore, agnostic to the intentions of the pilots and ATCOs, which can have a
significant effect on the observed trajectory, particularly in the lateral
plane. This work proposes a generative method for lateral TP, using
probabilistic machine learning to model the effect of the epistemic uncertainty
arising from the unknown effect of pilot behaviour and ATCO intentions. The
models are trained to be specific to a particular sector, allowing local
procedures such as coordinated entry and exit points to be modelled. A dataset
comprising a week's worth of aircraft surveillance data, passing through a busy
sector of the United Kingdom's upper airspace, was used to train and test the
models. Specifically, a piecewise linear model was used as a functional,
low-dimensional representation of the ground tracks, with its control points
determined by a generative model conditioned on partial context. It was found
that, of the investigated models, a Bayesian Neural Network using the Laplace
approximation was able to generate the most plausible trajectories in order to
emulate the flow of traffic through the sector
DE-TGN: Uncertainty-Aware Human Motion Forecasting using Deep Ensembles
Ensuring the safety of human workers in a collaborative environment with
robots is of utmost importance. Although accurate pose prediction models can
help prevent collisions between human workers and robots, they are still
susceptible to critical errors. In this study, we propose a novel approach
called deep ensembles of temporal graph neural networks (DE-TGN) that not only
accurately forecast human motion but also provide a measure of prediction
uncertainty. By leveraging deep ensembles and employing stochastic Monte-Carlo
dropout sampling, we construct a volumetric field representing a range of
potential future human poses based on covariance ellipsoids. To validate our
framework, we conducted experiments using three motion capture datasets
including Human3.6M, and two human-robot interaction scenarios, achieving
state-of-the-art prediction error. Moreover, we discovered that deep ensembles
not only enable us to quantify uncertainty but also improve the accuracy of our
predictions
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
The ability to estimate epistemic uncertainty is often crucial when deploying
machine learning in the real world, but modern methods often produce
overconfident, uncalibrated uncertainty predictions. A common approach to
quantify epistemic uncertainty, usable across a wide class of prediction
models, is to train a model ensemble. In a naive implementation, the ensemble
approach has high computational cost and high memory demand. This challenges in
particular modern deep learning, where even a single deep network is already
demanding in terms of compute and memory, and has given rise to a number of
attempts to emulate the model ensemble without actually instantiating separate
ensemble members. We introduce FiLM-Ensemble, a deep, implicit ensemble method
based on the concept of Feature-wise Linear Modulation (FiLM). That technique
was originally developed for multi-task learning, with the aim of decoupling
different tasks. We show that the idea can be extended to uncertainty
quantification: by modulating the network activations of a single deep network
with FiLM, one obtains a model ensemble with high diversity, and consequently
well-calibrated estimates of epistemic uncertainty, with low computational
overhead in comparison. Empirically, FiLM-Ensemble outperforms other implicit
ensemble methods, and it and comes very close to the upper bound of an explicit
ensemble of networks (sometimes even beating it), at a fraction of the memory
cost.Comment: accepted at NeurIPS 202
Sparse Bayesian neural networks for regression: Tackling overfitting and computational challenges in uncertainty quantification
Neural networks (NNs) are primarily developed within the frequentist
statistical framework. Nevertheless, frequentist NNs lack the capability to
provide uncertainties in the predictions, and hence their robustness can not be
adequately assessed. Conversely, the Bayesian neural networks (BNNs) naturally
offer predictive uncertainty by applying Bayes' theorem. However, their
computational requirements pose significant challenges. Moreover, both
frequentist NNs and BNNs suffer from overfitting issues when dealing with noisy
and sparse data, which render their predictions unwieldy away from the
available data space. To address both these problems simultaneously, we
leverage insights from a hierarchical setting in which the parameter priors are
conditional on hyperparameters to construct a BNN by applying a semi-analytical
framework known as nonlinear sparse Bayesian learning (NSBL). We call our
network sparse Bayesian neural network (SBNN) which aims to address the
practical and computational issues associated with BNNs. Simultaneously,
imposing a sparsity-inducing prior encourages the automatic pruning of
redundant parameters based on the automatic relevance determination (ARD)
concept. This process involves removing redundant parameters by optimally
selecting the precision of the parameters prior probability density functions
(pdfs), resulting in a tractable treatment for overfitting. To demonstrate the
benefits of the SBNN algorithm, the study presents an illustrative regression
problem and compares the results of a BNN using standard Bayesian inference,
hierarchical Bayesian inference, and a BNN equipped with the proposed
algorithm. Subsequently, we demonstrate the importance of considering the full
parameter posterior by comparing the results with those obtained using the
Laplace approximation with and without NSBL