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Interpretability methods for deep neural networks mainly focus on the sensitivity of the class score with respect to the original or perturbed input, usually measured using actual or modified gradients. Some methods also use a…

Computer Vision and Pattern Recognition · Computer Science 2022-06-20 Md Mahfuzur Rahman , Noah Lewis , Sergey Plis

STEM Mental models can play a critical role in assessing students' conceptual understanding of a topic. They not only offer insights into what students know but also into how effectively they can apply, relate to, and integrate concepts…

Computers and Society · Computer Science 2026-03-03 Pritam Sil , Durgaprasad Karnam , Vinay Reddy Venumuddala , Pushpak Bhattacharyya

We consider the problem of explaining the decisions of deep neural networks for image recognition in terms of human-recognizable visual concepts. In particular, given a test set of images, we aim to explain each classification in terms of a…

Machine Learning · Computer Science 2018-12-21 Mandana Hamidi-Haines , Zhongang Qi , Alan Fern , Fuxin Li , Prasad Tadepalli

In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results. We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval…

Information Retrieval · Computer Science 2019-12-11 Sebastian Hofstätter , Markus Zlabinger , Allan Hanbury

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Emily Kaczmarek , Olivier X. Miguel , Alexa C. Bowie , Robin Ducharme , Alysha L. J. Dingwall-Harvey , Steven Hawken , Christine M. Armour , Mark C. Walker , Kevin Dick

Large-scale dense mapping is vital in robotics, digital twins, and virtual reality. Recently, implicit neural mapping has shown remarkable reconstruction quality. However, incremental large-scale mapping with implicit neural representations…

Robotics · Computer Science 2024-04-10 Jianheng Liu , Haoyao Chen

Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit…

Neurons and Cognition · Quantitative Biology 2022-07-26 Hejie Cui , Wei Dai , Yanqiao Zhu , Xiaoxiao Li , Lifang He , Carl Yang

Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching. Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model…

Information Retrieval · Computer Science 2021-06-10 Zhiwen Tang , Grace Hui Yang

Transformer networks, particularly those achieving performance comparable to GPT models, are well known for their robust feature extraction abilities. However, the nature of these extracted features and their alignment with human-engineered…

Information Retrieval · Computer Science 2025-07-23 Tanya Chowdhury , Atharva Nijasure , James Allan

Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we…

Computation and Language · Computer Science 2019-05-21 Reza Ghaeini , Xiaoli Z. Fern , Prasad Tadepalli

Explainable AI(XAI)is a domain focused on providing interpretability and explainability of a decision-making process. In the domain of law, in addition to system and data transparency, it also requires the (legal-) decision-model…

Human-Computer Interaction · Computer Science 2020-12-18 Lukasz Gorski , Shashishekar Ramakrishna , Jedrzej M. Nowosielski

Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing test-time compute to enhance reward model performance. In…

Computation and Language · Computer Science 2025-05-21 Jiaxin Guo , Zewen Chi , Li Dong , Qingxiu Dong , Xun Wu , Shaohan Huang , Furu Wei

The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these…

Artificial Intelligence · Computer Science 2023-01-03 Lars Holmberg , Paul Davidsson , Per Linde

In this work, we introduce a new method for imitation learning from video demonstrations. Our method, Relational Mimic (RM), improves on previous visual imitation learning methods by combining generative adversarial networks and relational…

Machine Learning · Computer Science 2019-12-19 Lionel Blondé , Yichuan Charlie Tang , Jian Zhang , Russ Webb

The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because…

Artificial Intelligence · Computer Science 2023-07-12 Bang Chen , Wei Peng , Maonian Wu , Bo Zheng , Shaojun Zhu

Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering RNN-based approaches is improving their explainability and interpretability. In this work we present MEME: a model…

Machine Learning · Computer Science 2021-04-15 Dmitry Kazhdan , Botty Dimanov , Mateja Jamnik , Pietro Liò

Recent advances in neural word embedding provide significant benefit to various information retrieval tasks. However as shown by recent studies, adapting the embedding models for the needs of IR tasks can bring considerable further…

Information Retrieval · Computer Science 2018-04-05 Navid Rekabsaz , Bhaskar Mitra , Mihai Lupu , Allan Hanbury

The Convolutional Neural Network (CNN) is a widely used deep learning architecture for computer vision. However, its black box nature makes it difficult to interpret the behavior of the model. To mitigate this issue, AI practitioners have…

Computer Vision and Pattern Recognition · Computer Science 2023-03-14 Seok-Yong Byun , Wonju Lee

The nonliteral interpretation of a text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspired by human reading comprehension, we propose a novel,…

Computation and Language · Computer Science 2020-01-17 Guoxiu He , Zhe Gao , Zhuoren Jiang , Yangyang Kang , Changlong Sun , Xiaozhong Liu , Wei Lu

We present a comprehensive study of answer quality evaluation in Retrieval-Augmented Generation (RAG) applications using vRAG-Eval, a novel grading system that is designed to assess correctness, completeness, and honesty. We further map the…

Computation and Language · Computer Science 2024-11-08 Yang Wang , Alberto Garcia Hernandez , Roman Kyslyi , Nicholas Kersting