Search Results

To address the barrier caused by the black-box nature of Deep Learning (DL) for practical deployment, eXplainable Artificial Intelligence (XAI) has emerged and is developing rapidly. While significant progress has been made in explanation techniques for ...

An important aspect of responsible recommendation systems is the transparency of the prediction mechanisms. This is a general challenge for deep-learning-based systems such as the currently predominant neural news recommender architectures, which are ...

Tree models have made an impressive progress during the past years, while an important problem is to understand how these models predict, in particular for critical applications such as finance and medicine. For this issue, most previous works ...

The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for ...

We present MIRACLE, a system for online, interpretable visual concept and video action recognition. Through a chat interface, users query the recognition system with an uploaded image or video. For images, MIRACLE returns concept predictions from its ...

By integrating various modules with the Visual Transformer (ViT), we facilitate a interpretation of image processing across each layer and attention head. This method allows us to explore the connections both within and across the layers, enabling a ...

Sign language recognition (SLR) refers to interpreting sign language glosses from given videos automatically. This research area presents a complex challenge in computer vision because of the rapid and intricate movements inherent in sign languages, ...

In the field of artificial intelligence, AI models are frequently described as 'black boxes' due to the obscurity of their internal mechanisms. It has ignited research interest on model interpretability, especially in attribution methods that offers ...

In this paper, we delve into the concept of interpretable image enhancement, a technique that enhances image quality by adjusting filter parameters with easily understandable names such as "Exposure'' and "Contrast''. Unlike using predefined image ...

Code-trained language models have proven to be highly effective for various code intelligence tasks. However, they can be challenging to train and deploy for many software engineering applications due to computational bottlenecks and memory constraints. ...

In real-world microservice systems, the newly deployed service - one kind of change service, could lead to a new type of minority fault. Existing state-of-the-art (SOTA) methods for fault localization rarely consider the imbalanced fault classification ...

Long-Form Question Answering (LFQA) represents a growing interest in Legal Natural Language Processing (Legal-NLP) as many individuals encounter legal disputes at some point in their lives, but lack of knowledge about how to negotiate these complex ...

Survival analysis models time-to-event distributions with censorship. Recently, deep survival models using neural networks have dominated due to their representational power and state-of-the-art performance. However, their "black-box" nature hinders ...

As machine learning (ML) models and datasets increase in complexity, the demand for methods that enhance explainability and interpretability becomes paramount. Prototypes, by encapsulating essential characteristics within data, offer insights that enable ...

Out-of-distribution (OOD) aware classification aims to classify in-distribution samples into their respective classes while simultaneously detecting OOD samples. Previous works have largely focused on the image domain, where images from an unrelated ...

Numerous explanation methods have been recently developed to interpret the decisions made by deep neural network (DNN) models. For image classifiers, these methods typically provide an attribution score to each pixel in the image to quantify its ...

Dual encoders are highly effective and widely deployed in the retrieval phase for passage and document ranking, question answering, or retrieval-augmented generation (RAG) setups. Most dual-encoder models use transformer models like BERT to map input ...

Responsible AI is built upon a set of principles that prioritize fairness, transparency, accountability, and inclusivity in AI development and deployment. As AI systems become increasingly sophisticated, including the explosion of generative AI, there is ...

Ensuring that Machine Learning (ML) models make correct and meaningful inferences is necessary for the broader adoption of such models into high-stakes decision-making scenarios. Thus, ML model engineers increasingly use eXplainable AI (XAI) tools to ...

Improved thyroid nodule risk stratification from ultrasound (US) can mitigate overdiagnosis and unnecessary biopsies. Previous studies often train deep learning models using manually selected single US frames; these approaches deviate from ...