123,829 research outputs found
Structured uncertainty prediction networks
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image. Previous approaches have been mostly limited to predicting diagonal covariance matrices. Our novel model learns to predict a full Gaussian covariance matrix for each reconstruction, which permits efficient sampling and likelihood evaluation.
We demonstrate that our model can accurately reconstruct ground truth correlated residual distributions for synthetic datasets and generate plausible high frequency samples for real face images. We also illustrate the use of these predicted covariances for structure preserving image denoising
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization
Reliable uncertainty quantification in deep neural networks is very crucial
in safety-critical applications such as automated driving for trustworthy and
informed decision-making. Assessing the quality of uncertainty estimates is
challenging as ground truth for uncertainty estimates is not available.
Ideally, in a well-calibrated model, uncertainty estimates should perfectly
correlate with model error. We propose a novel error aligned uncertainty
optimization method and introduce a trainable loss function to guide the models
to yield good quality uncertainty estimates aligning with the model error. Our
approach targets continuous structured prediction and regression tasks, and is
evaluated on multiple datasets including a large-scale vehicle motion
prediction task involving real-world distributional shifts. We demonstrate that
our method improves average displacement error by 1.69% and 4.69%, and the
uncertainty correlation with model error by 17.22% and 19.13% as quantified by
Pearson correlation coefficient on two state-of-the-art baselines.Comment: Accepted to ECCV 2022 workshop - Safe Artificial Intelligence for
Automated Drivin
A New PHO-rmula for Improved Performance of Semi-Structured Networks
Recent advances to combine structured regression models and deep neural
networks for better interpretability, more expressiveness, and statistically
valid uncertainty quantification demonstrate the versatility of semi-structured
neural networks (SSNs). We show that techniques to properly identify the
contributions of the different model components in SSNs, however, lead to
suboptimal network estimation, slower convergence, and degenerated or erroneous
predictions. In order to solve these problems while preserving favorable model
properties, we propose a non-invasive post-hoc orthogonalization (PHO) that
guarantees identifiability of model components and provides better estimation
and prediction quality. Our theoretical findings are supported by numerical
experiments, a benchmark comparison as well as a real-world application to
COVID-19 infections.Comment: ICML 202
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
To improve uncertainty quantification of variance networks, we propose a
novel tree-structured local neural network model that partitions the feature
space into multiple regions based on uncertainty heterogeneity. A tree is built
upon giving the training data, whose leaf nodes represent different regions
where region-specific neural networks are trained to predict both the mean and
the variance for quantifying uncertainty. The proposed Uncertainty-Splitting
Neural Regression Tree (USNRT) employs novel splitting criteria. At each node,
a neural network is trained on the full data first, and a statistical test for
the residuals is conducted to find the best split, corresponding to the two
sub-regions with the most significant uncertainty heterogeneity. USNRT is
computationally friendly because very few leaf nodes are sufficient and pruning
is unnecessary. On extensive UCI datasets, in terms of both calibration and
sharpness, USNRT shows superior performance compared to some recent popular
methods for variance prediction, including vanilla variance network, deep
ensemble, dropout-based methods, tree-based models, etc. Through comprehensive
visualization and analysis, we uncover how USNRT works and show its merits
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates,
limiting their reliable deployment in settings where the cost of errors is
significant. We propose conformalized GNN (CF-GNN), extending conformal
prediction (CP) to graph-based models for guaranteed uncertainty estimates.
Given an entity in the graph, CF-GNN produces a prediction set/interval that
provably contains the true label with pre-defined coverage probability (e.g.
90%). We establish a permutation invariance condition that enables the validity
of CP on graph data and provide an exact characterization of the test-time
coverage. Moreover, besides valid coverage, it is crucial to reduce the
prediction set size/interval length for practical use. We observe a key
connection between non-conformity scores and network structures, which
motivates us to develop a topology-aware output correction model that learns to
update the prediction and produces more efficient prediction sets/intervals.
Extensive experiments show that CF-GNN achieves any pre-defined target marginal
coverage while significantly reducing the prediction set/interval size by up to
74% over the baselines. It also empirically achieves satisfactory conditional
coverage over various raw and network features.Comment: Published at NeurIPS 202
- …