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We propose a scalable perception framework leveraging monocular security cameras in the infrastructure for localizing and tracking indoor autonomous mobile robots. We present a zero-shot pose estimation approach that combines semantic and visual ...

Radiance fields are powerful and, hence, popular models for representing the appearance of complex scenes. Yet, constructing them based on image observations gives rise to ambiguities and uncertainties. We propose a versatile approach for learning ...

Recently, Knowledge Graphs (KGs) have been successfully coupled with Large Language Models (LLMs) to mitigate their hallucinations and enhance their reasoning capability, e.g., KG-based retrieval-augmented framework for question-answering. However, ...

Time Series Segmentation (TSS) is a data mining task widely used in many applications to generate a set of change points for a time series. Current TSS performance analyses focus on accuracy and, therefore, fail to fully evaluate the reliability and ...

We present a novel method for explainable vertebral fracture assessment (XVFA) in low-dose radiographs using deep neural networks, incorporating vertebra detection and keypoint localization with uncertainty estimates. We incorporate Genant’s semi-...

Uncertainty quantification is emerging as a critical tool in high-stakes decision-making processes, where trust in automated predictions that lack accuracy and precision can be time-consuming and costly. In drug discovery, such high-stakes ...

Recently, a surge of interest has been given to quantifying epistemic uncertainty (EU), the reducible portion of uncertainty due to lack of data. We propose a novel EU estimator in the binary classification setting, as the posterior expected ...

We propose conformal hyperrectangular prediction regions for multi‐target regression. We propose split conformal prediction algorithms for both point and quantile regression to form hyperrectangular prediction regions, which allow for easy ...

In this work, we study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. Previously, the so-called Graph Posterior Network (GPN) model has been proposed to quantify ...

Despite their remarkable ability to understand both textual and visual data, large vision-language models (LVLMs) still face issues with hallucination. This is particularly presented as the object hallucination, where the models inaccurately ...

Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. Although numerous uncertainty quantification methods have been proposed to address this limitation, they ...

Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios. ...

Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model. However, the ...

Optimal values and solutions of empirical approximations of stochastic optimization problems can be viewed as statistical estimators of their true values. From this perspective, it is important to understand the asymptotic behavior of these estimators as ...

Bayesian optimisation (BO) is a popular tool for solving expensive optimisation problems. BO utilises Bayesian models and balances exploitation and exploration in searching for potential solutions. In this work, we investigate the trade-off between ...

Sequential recommendation systems suggest products based on users' historical behaviours. The inherent sparsity of user-item interactions in a vast product space often leads to unreliable recommendations. Recent research addresses this challenge by ...

Hardware faults in AI accelerators, particularly in accelerator memory, can alter pre-trained deep neural network parameters, leading to errors that compromise performance. To address this, just-intime (JIT) fault detection and mitigation are crucial. ...

Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In this paper, ...

Patient risk prediction models are crucial as they enable healthcare providers to proactively identify and address potential health risks. Large pre-trained foundation models offer remarkable performance in risk prediction tasks by analyzing multimodal ...

Click-through Rate (CTR) module is the foundation block of recommendation system and used for search, content selection, advertising, video streaming etc. CTR is modelled as a classification problem and extensive research is done to improve the CTR ...