相关论文: Proceedings of AAAI 2019 Workshop on Network Inter…

This third international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop…

Proceedings of the 1st International Workshop on Robot Learning and Planning (RLP 2016)

This volume contains a selection of papers presented at the 16th International Workshop on the ACL2 Theorem Prover and its Applications (ACL2-2020). The workshops are the premier technical forum for presenting research and experiences…

Methods for understanding the decisions of and mechanisms underlying deep neural networks (DNNs) typically rely on building intuition by emphasizing sensory or semantic features of individual examples. For instance, methods aim to visualize…

This work makes a substantial step in the field of split computing, i.e., how to split a deep neural network to host its early part on an embedded device and the rest on a server. So far, potential split locations have been identified…

As an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network's structural and dynamical attributes for utilization, network…

Deep neural networks for medical image classification often fail to generalize consistently in clinical practice due to violations of the i.i.d. assumption and opaque decision-making. This paper examines interpretability in deep neural…

Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models. These models, however, are generally regarded as black boxes, which, in spite of…

My notes on Deep Learning for NLP.

Communication networks are becoming increasingly complex towards 6G. Manual management is no longer an option for network operators. Network automation has been widely discussed in the networking community, and it is a sensible means to…

Machine learning is frequently used in affective computing, but presents challenges due the opacity of state-of-the-art machine learning methods. Because of the impact affective machine learning systems may have on an individual's life, it…

The techniques of deep learning have become the state of the art methodology for executing complicated tasks from various domains of computer vision, natural language processing, and several other areas. Due to its rapid development and…

This report synthesizes the outcomes of a recent interdisciplinary workshop that brought together leading experts in cognitive psychology, language learning, and artificial intelligence (AI)-based natural language processing (NLP). The…

The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intelligence (AI) is the use of saliency or heat maps highlighting…

This volume contains a selection of papers presented at the 17th International Workshop on the ACL2 Theorem Prover and its Applications (ACL2 2022). The workshops are the premier technical forum for presenting research and experiences…

Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods applied to time series as time series by nature can not be…

This paper is a write-up for the tutorial on "Fine-grained Interpretation and Causation Analysis in Deep NLP Models" that we are presenting at NAACL 2021. We present and discuss the research work on interpreting fine-grained components of a…

Proceedings of the BMVC 2019 Workshop on Interpretable and Explainable Machine Vision, Cardiff, UK, September 12, 2019.

The inherent "black box" nature of deep neural networks (DNNs) compromises their transparency and reliability. Recently, explainable AI (XAI) has garnered increasing attention from researchers. Several perturbation-based interpretations…

Interpretability of Deep Neural Networks (DNNs) is a growing field driven by the study of vision and language models. Yet, some use cases, like image captioning, or domains like Deep Reinforcement Learning (DRL), require complex modelling,…