Related papers: BMVC 2019: Workshop on Interpretable and Explainab…

This volume represents the proceedings of the International Workshop on Reconfigurable and Communication-centric Cyber-Physical Systems (ReCoCyPS 2019), co-located with the 14th International Symposium on Reconfigurable…

There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-specific terms can be crucial for adoption in risk-sensitive…

This volume contains a final and revised selection of papers presented at the Seventh International Workshop on Verification and Program Transformation (VPT 2019), which took place in Genova, Italy, on April 2nd, 2019, affiliated with…

This record contains the proceedings of the 2020 Workshop on Assessing, Explaining, and Conveying Robot Proficiency for Human-Robot Teaming, which was held in conjunction with the 2020 ACM/IEEE International Conference on Human-Robot…

This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle…

This volume contains a selection of papers presented at LFMTP 2018, the 13th international Workshop on Logical Frameworks and Meta-Languages: Theory and Practice (LFMTP), held on July 7, 2018, in Oxford, UK. The workshop was affiliated with…

This volume represents the proceedings of the 5th Workshop on Membrane Computing and Biologically Inspired Process Calculi (MeCBIC 2011), held together with the 12th International Conference on Membrane Computing on 23rd August 2011 in…

This volume contains the papers presented at the 6th Membrane Computing and Biologically Inspired Process Calculi (MeCBIC 2012), a satellite workshop of the 23rd International Conference on Concurrency Theory (CONCUR) held on 8th September…

Mechanistic interpretability seeks to understand the neural mechanisms that enable specific behaviors in Large Language Models (LLMs) by leveraging causality-based methods. While these approaches have identified neural circuits that copy…

As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other…

In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics,…

In the context of image classification, Concept Bottleneck Models (CBMs) first embed images into a set of human-understandable concepts, followed by an intrinsically interpretable classifier that predicts labels based on these intermediate…

Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but extending this paradigm to video remains challenging due to the difficulty of extracting…

Highly Automated Vehicles (HAVs) can improve mobility for blind and visually impaired people (BVIPs). However, designing non-visual interfaces that enable them to maintain situation awareness inside the vehicle is a challenge. This paper…

Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision. They have shown promising results over convolution neural networks in fundamental computer vision…

This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic…

Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet…

Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretability, making the behaviors of the deep networks difficult to…

These are the proceedings of the Second International Workshop on Safe Control of Autonomous Vehicles, which took place on the 10th of April 2018 in Porto, Portugal as an affiliated workshop of CSPWeek. The task of this workshop is to…

Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging. However, the lack of detailed investigations into the…