Artificial Intelligence (AI) systems, particularly neural networks, are increasingly employed in critical applications such as healthcare, finance, and autonomous systems. These systems play an integral role in decision-making, where the trustworthiness of their predictions becomes paramount. Trustworthiness in AI encompasses attributes like reliability, robustness, fairness, and transparency, yet these qualities are often difficult to evaluate, particularly in neural networks, which are typically viewed as "black-box" models. This opacity raises significant concerns about their trustworthiness, especially in sensitive domains where incorrect decisions can lead to severe consequences. Traditional performance metrics like accuracy, precision, and recall measure only the correctness of the model's predictions but fail to capture the confidence and uncertainty associated with those predictions. Confidence calibration, which aligns predicted probabilities with actual outcomes, has emerged as an important tool to address these shortcomings. Well-calibrated models provide predictions where the predicted probability corresponds to the actual likelihood of the event, ensuring that a 70% confidence means the event occurs approximately 70% of the time. However, despite its utility, calibration alone does not fully address the issue of trustworthiness, as it does not account for subjective uncertainty or provide an interpretable way to assess trust across a range of predictions. To address this, we propose the use of subjective logic for trustworthiness quantification in neural networks.

Subjective Logic (SL) is a logic incorporating uncertainty and opinions for agents in dynamic systems. In this work, we investigate the use of subjective logic to model opinions and belief change in social networks. In particular, we work toward the development of a subjective logic belief/opinion update function appropriate for modeling belief change as communication occurs in social networks. We found through experiments that an update function with belief fusion from SL does not have ideal properties to represent a rational update. Even without these properties, we found that an update function with cumulative belief fusion can describe behaviors not explored by the social network model defined by Alvim, Knight, and Valencia (2019).

The resulting neural network has only logarithmic depth (alleviating the degradation of data as it propagates through the network) and can process sequences in linear time (or in logarithmic time with a linear number of processors). The crucial property of FENs is that they learn by training a quasi-linear number of constant-depth feed-forward neural networks in parallel. The fact that these networks have constant depth means that backpropagation works well. We note that currently the performance of FENs is only conjectured as we are yet to implement them.

Modeling 3D context is essential for high-performance 3D medical image analysis. Although 2D networks benefit from large-scale 2D supervised pretraining, it is weak in capturing 3D context. 3D networks are strong in 3D context yet lack supervised pretraining. As an emerging technique, \emph{3D context fusion operator}, which enables conversion from 2D pretrained networks, leverages the advantages of both and has achieved great success. Existing 3D context fusion operators are designed to be spatially symmetric, i.e., performing identical operations on each 2D slice like convolutions. However, these operators are not truly equivariant to translation, especially when only a few 3D slices are used as inputs. In this paper, we propose a novel asymmetric 3D context fusion operator (A3D), which uses different weights to fuse 3D context from different 2D slices. Notably, A3D is NOT translation-equivariant while it significantly outperforms existing symmetric context fusion operators without introducing large computational overhead. We validate the effectiveness of the proposed method by extensive experiments on DeepLesion benchmark, a large-scale public dataset for universal lesion detection from computed tomography (CT). The proposed A3D consistently outperforms symmetric context fusion operators by considerable margins, and establishes a new \emph{state of the art} on DeepLesion. To facilitate open research, our code and model in PyTorch are available at https://github.com/M3DV/AlignShift.

In decentralised autonomous systems it is the interactions between individual agents which govern the collective behaviours of the system. These local-level interactions are themselves often governed by an underlying network structure. These networks are particularly important for collective learning and decision-making whereby agents must gather evidence from their environment and propagate this information to other agents in the system. Models for collective behaviours may often rely upon the assumption of total connectivity between agents to provide effective information sharing within the system, but this assumption may be ill-advised. In this paper we investigate the impact that the underlying network has on performance in the context of collective learning. Through simulations we study small-world networks with varying levels of connectivity and randomness and conclude that totally-connected networks result in higher average error when compared to networks with less connectivity. Furthermore, we show that networks of high regularity outperform networks with increasing levels of random connectivity.

The purpose of multi-source fusion is to combine information from more than two evidence sources, or subjective opinions from multiple actors. For subjective logic, a number of different fusion operators have been proposed, each matching a fusion scenario with different assumptions. However, not all of these operators are associative, and therefore multi-source fusion is not well-defined for these settings. In this paper, we address this challenge, and define multi-source fusion for weighted belief fusion (WBF) and consensus & compromise fusion (CCF). For WBF, we show the definition to be equivalent to the intuitive formulation under the bijective mapping between subjective logic and Dirichlet evidence PDFs. For CCF, since there is no independent generalization, we show that the resulting multi-source fusion produces valid opinions, and explain why our generalization is sound. For completeness, we also provide corrections to previous results for averaging and cumulative belief fusion (ABF and CBF), as well as belief constraint fusion (BCF), which is an extension of Dempster's rule. With our generalizations of fusion operators, fusing information from multiple sources is now well-defined for all different fusion types defined in subjective logic. This enables wider applicability of subjective logic in applications where multiple actors interact.

Multimodal applications offer a challenge to model selection in machine learning, as the interactions between different data modalities (e.g., between video and audio, or between images and questions) may require a complicated prior in order to accurately capture regularities necessary for downstream tasks. The particular multimodal application that we consider in this work is that of visual question-answering (VQA), i.e., of producing a natural language response to the combined input consisting of an image and a natural language question pertaining specifically to that image. In the case of VQA, the complexity of the task exists in both extracting useful feature representations from the question and the image, as well as the "fusion" of these feature representations by combining them in order to predict the answer to the question. In this paper, we focus on the problem of combining feature representations in the VQA task through models based on a generic "fusion operator" definition. We illustrate the complexity of model selection for the VQA task by designing a class of multimodal fusion operators, each of which combines raw question and visual data streams to predict answers to the given questions based on an image. We evaluate specific instances of high performing fusion operators belonging to the same design class. We evaluate and discuss three multimodal fusion architectural components that emerge as improving performance as part of the investigation into the general class of fusion operators: 1) The use of a gating mechanism, wherein individual features extracted by the fusion operator are turned on or off by a multiplicative interaction.

Belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources. Depending on the situation to be modelled, different belief fusion methods can be applied. Cumulative and averaging belief fusion is defined for fusing opinions in subjective logic, and for fusing belief functions in general. The principle of fission is the opposite of fusion, namely to eliminate the contribution of a specific belief from an already fused belief, with the purpose of deriving the remaining belief. This paper describes fission of cumulative belief as well as fission of averaging belief in subjective logic. These operators can for example be applied to belief revision in Bayesian belief networks, where the belief contribution of a given evidence source can be determined as a function of a given fused belief and its other contributing beliefs.