標(biāo)題: Titlebook: Explainable and Interpretable Models in Computer Vision and Machine Learning; Hugo Jair Escalante,Sergio Escalera,Marcel‘van Ger Book 2018 [打印本頁] 作者: infection 時(shí)間: 2025-3-21 19:01
書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning影響因子(影響力)
書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning影響因子(影響力)學(xué)科排名
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書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning被引頻次
書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning被引頻次學(xué)科排名
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書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning讀者反饋
書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning讀者反饋學(xué)科排名
作者: ostracize 時(shí)間: 2025-3-21 23:49 作者: –DOX 時(shí)間: 2025-3-22 00:49 作者: Carcinogen 時(shí)間: 2025-3-22 06:46
Bruno Allevato,Suzana Kahn Ribeirofor their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is little consensus on what interpretable machine learning is and how it should be measured and evaluated. In thi作者: glans-penis 時(shí)間: 2025-3-22 12:29 作者: insurrection 時(shí)間: 2025-3-22 16:32 作者: insurrection 時(shí)間: 2025-3-22 18:53
https://doi.org/10.1007/978-3-031-18448-2ted with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analy作者: 愛好 時(shí)間: 2025-3-23 01:02
Handbook of Top Management Teamssimpler models are more interpretable than more complex models with higher performance. In practice, one can choose a readily interpretable (possibly less predictive) model. Another solution is to directly explain the original, highly predictive model. In this chapter, we present a middle-ground app作者: NAV 時(shí)間: 2025-3-23 04:38 作者: 斜 時(shí)間: 2025-3-23 07:06
Daniel R. Williams,Norman McIntyre of multiple models. Also, the top-ranked systems in many data-mining and computer vision competitions use ensembles. Although ensembles are popular, they are opaque and hard to interpret. Explanations make AI systems more transparent and also justify their predictions. However, there has been littl作者: 推測(cè) 時(shí)間: 2025-3-23 12:21
Ahu Yazici Ayyildiz,Erdogan Koc provide easy-to-interpret rationales for their behavior—so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here, we explore the use of visual explanations. These explanations take the form of real-time highlighted regions 作者: 弓箭 時(shí)間: 2025-3-23 15:59 作者: forecast 時(shí)間: 2025-3-23 22:03
Miranda Giacomin,Christian H. Jordanticular, assessment of apparent personality gives insights into the first impressions evoked by a candidate. Such analysis tools can be used for training purposes, if they can be configured to provide appropriate and clear feedback. In this chapter, we describe a multimodal system that analyzes a sh作者: Surgeon 時(shí)間: 2025-3-24 01:49
Essential Cerebrovascular Hemodynamics,man-machine interactions. Expressing the rationale behind such a system’s output is an important aspect of human-machine interaction as AI continues to be prominent in general, everyday use-cases. In this paper, we introduce a novel framework integrating Grenander’s pattern theory structures to prod作者: obstinate 時(shí)間: 2025-3-24 04:16
Springer Nature Switzerland AG 2018作者: wangle 時(shí)間: 2025-3-24 10:00
Explainable and Interpretable Models in Computer Vision and Machine Learning978-3-319-98131-4Series ISSN 2520-131X Series E-ISSN 2520-1328 作者: 追蹤 時(shí)間: 2025-3-24 13:37 作者: Analogy 時(shí)間: 2025-3-24 16:36
Learning Interpretable Rules for Multi-Label Classificationel data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.作者: 反感 時(shí)間: 2025-3-24 22:47 作者: 包租車船 時(shí)間: 2025-3-25 01:26 作者: aesthetic 時(shí)間: 2025-3-25 04:28 作者: Etymology 時(shí)間: 2025-3-25 08:49 作者: mastoid-bone 時(shí)間: 2025-3-25 14:11
Structuring Neural Networks for More Explainable Predictionsroach where the original neural network architecture is modified parsimoniously in order to reduce common biases observed in the explanations. Our approach leads to explanations that better separate classes in feed-forward networks, and that also better identify relevant time steps in recurrent neural networks.作者: AGGER 時(shí)間: 2025-3-25 17:54 作者: 饑荒 時(shí)間: 2025-3-25 20:18 作者: 虛弱的神經(jīng) 時(shí)間: 2025-3-26 00:11 作者: GUILE 時(shí)間: 2025-3-26 05:12 作者: 支架 時(shí)間: 2025-3-26 11:44
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are 作者: 字的誤用 時(shí)間: 2025-3-26 14:56 作者: expository 時(shí)間: 2025-3-26 18:35
Learning Interpretable Rules for Multi-Label Classificationted with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analy作者: jocular 時(shí)間: 2025-3-26 23:42 作者: CHAR 時(shí)間: 2025-3-27 04:39 作者: Override 時(shí)間: 2025-3-27 07:56
Ensembling Visual Explanations of multiple models. Also, the top-ranked systems in many data-mining and computer vision competitions use ensembles. Although ensembles are popular, they are opaque and hard to interpret. Explanations make AI systems more transparent and also justify their predictions. However, there has been littl作者: 草率男 時(shí)間: 2025-3-27 12:27 作者: Palatial 時(shí)間: 2025-3-27 15:56 作者: CHOP 時(shí)間: 2025-3-27 18:45
Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisionsticular, assessment of apparent personality gives insights into the first impressions evoked by a candidate. Such analysis tools can be used for training purposes, if they can be configured to provide appropriate and clear feedback. In this chapter, we describe a multimodal system that analyzes a sh作者: 記憶 時(shí)間: 2025-3-27 22:43 作者: Mortar 時(shí)間: 2025-3-28 06:06 作者: expire 時(shí)間: 2025-3-28 06:49
Cooling System Study and Simulationare missing and we speculate which criteria these methods/interfaces should satisfy. Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.作者: 北極人 時(shí)間: 2025-3-28 10:52 作者: HUMP 時(shí)間: 2025-3-28 15:10
Injazz J. Chen,Kenneth A. Paetschiscriminative than descriptions produced by existing captioning methods. In this work, we emphasize the importance of producing an explanation for an observed action, which could be applied to a black-box decision agent, akin to what one human produces when asked to explain the actions of a second h作者: 啜泣 時(shí)間: 2025-3-28 20:56
Daniel R. Williams,Norman McIntyrerowd-sourced human evaluation indicates that our ensemble visual explanation is significantly qualitatively outperform each of the individual system’s visual explanation. Overall, our ensemble explanation is better 61. of the time when compared to any individual system’s explanation and is also suff作者: 過剩 時(shí)間: 2025-3-29 01:22
Ahu Yazici Ayyildiz,Erdogan Kocons actually influence the output. This produces more succinct visual explanations and more accurately exposes the network’s behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 h of driving. We first show that training with attention does not degrade the performance作者: epidermis 時(shí)間: 2025-3-29 04:35
Derek L. Milne,Robert P. Reiserf a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-sup作者: agenda 時(shí)間: 2025-3-29 09:08
