標(biāo)題: Titlebook: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning; Wojciech Samek,Grégoire Montavon,Klaus-Robert Müll Book 2019 Sprin [打印本頁] 作者: 無緣無故 時間: 2025-3-21 17:06
書目名稱Explainable AI: Interpreting, Explaining and Visualizing Deep Learning影響因子(影響力)
書目名稱Explainable AI: Interpreting, Explaining and Visualizing Deep Learning影響因子(影響力)學(xué)科排名
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書目名稱Explainable AI: Interpreting, Explaining and Visualizing Deep Learning被引頻次
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書目名稱Explainable AI: Interpreting, Explaining and Visualizing Deep Learning年度引用學(xué)科排名
書目名稱Explainable AI: Interpreting, Explaining and Visualizing Deep Learning讀者反饋
書目名稱Explainable AI: Interpreting, Explaining and Visualizing Deep Learning讀者反饋學(xué)科排名
作者: metropolitan 時間: 2025-3-21 23:18 作者: 無關(guān)緊要 時間: 2025-3-22 03:21 作者: Omniscient 時間: 2025-3-22 06:54
Understanding Neural Networks via Feature Visualization: A Surveys in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly. Those methods are known as Activation Maximization (AM) [.] or Feature Visualization via Optimization. In this chapter, we (1) review existing A作者: 壓碎 時間: 2025-3-22 10:20 作者: Indict 時間: 2025-3-22 12:54 作者: Indict 時間: 2025-3-22 18:43 作者: spinal-stenosis 時間: 2025-3-22 21:30
Explanations for Attributing Deep Neural Network Predictionsalthcare decision-making, there is a great need for . and . . of “why” an algorithm is making a certain prediction. In this chapter, we introduce 1. Meta-Predictors as Explanations, a principled framework for learning explanations for any black box algorithm, and 2. Meaningful Perturbations, an inst作者: Insensate 時間: 2025-3-23 05:21
Gradient-Based Attribution Methodsile several methods have been proposed to explain network predictions, the definition itself of explanation is still debated. Moreover, only a few attempts to compare explanation methods from a theoretical perspective has been done. In this chapter, we discuss the theoretical properties of several a作者: Console 時間: 2025-3-23 08:40 作者: Obvious 時間: 2025-3-23 10:03
Explaining and Interpreting LSTMshly heterogeneous due to the variety of tasks to be solved. In this chapter, we explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The speci作者: Juvenile 時間: 2025-3-23 15:49 作者: Coma704 時間: 2025-3-23 21:37
Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparisonses the question whether the produced explanations are reliable. In this chapter, we consider two popular explanation techniques, one based on gradient computation and one based on a propagation mechanism. We evaluate them using three “axiomatic” properties: ., ., and .. These properties are tested 作者: 格子架 時間: 2025-3-24 01:27 作者: Flawless 時間: 2025-3-24 05:09
https://doi.org/10.1007/978-3-030-03213-5put image) are responsible for a model’s output (i.e., a CNN classifier’s object class prediction). We first introduced these contributions in?[.]. We also briefly survey existing visual attribution methods and highlight how they faith to be both . and ..作者: Vertebra 時間: 2025-3-24 06:32
Kathy L. Bradley-Klug,Emily Shaffer-Hudkinsn the scenario of the train-from-scratch and in the stage of the fine-tuning between data sources. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.作者: 一起 時間: 2025-3-24 12:59 作者: COW 時間: 2025-3-24 16:24 作者: 強(qiáng)有力 時間: 2025-3-24 19:19
Book 2019ing factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For?sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper作者: oxidant 時間: 2025-3-25 01:49 作者: ingenue 時間: 2025-3-25 04:06 作者: 絕食 時間: 2025-3-25 07:54
Explaining and Interpreting LSTMsque used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The special accumulators and gated interactions present in the LSTM require both a new propagation scheme and an extension of the underlying theoretical framework to deliver faithful explanations.作者: cocoon 時間: 2025-3-25 11:44 作者: cringe 時間: 2025-3-25 17:32
0302-9743 urse and provides directions of future development.The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up hu作者: Canyon 時間: 2025-3-25 21:11 作者: molest 時間: 2025-3-26 01:23 作者: 不要嚴(yán)酷 時間: 2025-3-26 08:18
Michel Tenenhaus,Mohamed Hanafique used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The special accumulators and gated interactions present in the LSTM require both a new propagation scheme and an extension of the underlying theoretical framework to deliver faithful explanations.作者: 帳單 時間: 2025-3-26 09:53
Cancer-Related Pain in Childhood,t computation and one based on a propagation mechanism. We evaluate them using three “axiomatic” properties: ., ., and .. These properties are tested on the overall explanation, but also at intermediate layers, where our analysis brings further insights on how the explanation is being formed.作者: 引起 時間: 2025-3-26 12:39
Carol M. Trivette,Catherine P. Corrttribution methods and show how they share the same idea of using the gradient information as a descriptive factor for the functioning of a model. Finally, we discuss the strengths and limitations of these methods and compare them with available alternatives.作者: STYX 時間: 2025-3-26 19:38
Gradient-Based Attribution Methodsttribution methods and show how they share the same idea of using the gradient information as a descriptive factor for the functioning of a model. Finally, we discuss the strengths and limitations of these methods and compare them with available alternatives.作者: Individual 時間: 2025-3-26 21:42 作者: 價值在貶值 時間: 2025-3-27 01:57
Hedwig J. A. van Bakel,Ruby A. S. Halloretically justified as a ‘deep Taylor decomposition’, (3) how to choose the propagation rules at each layer to deliver high explanation quality, and (4) how LRP can be extended to handle a variety of machine learning scenarios beyond deep neural networks.作者: CAMP 時間: 2025-3-27 05:27
Seizure Classification and Semiology,The approach can be seen as a type of unit test; we construct a narrow ground truth to measure one stated desirable property. As such, we hope the community will embrace the development of additional tests.作者: Endometrium 時間: 2025-3-27 12:28
Towards Explainable Artificial Intelligence the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of . learning algorithms in practice.作者: 變白 時間: 2025-3-27 16:00
Layer-Wise Relevance Propagation: An Overvieworetically justified as a ‘deep Taylor decomposition’, (3) how to choose the propagation rules at each layer to deliver high explanation quality, and (4) how LRP can be extended to handle a variety of machine learning scenarios beyond deep neural networks.作者: 侵略 時間: 2025-3-27 20:17 作者: forager 時間: 2025-3-28 01:03
Examples of Archaeological Applications generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating the object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layo作者: 難聽的聲音 時間: 2025-3-28 03:29
Handbook of Parallel Constraint Reasoning-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Our IMSAT is able to discover interpretable representations that exhibit intended invariance. Extensive experiments on benchmark datasets show that IMSAT produc作者: municipality 時間: 2025-3-28 08:32 作者: BUOY 時間: 2025-3-28 14:09 作者: 頌揚(yáng)國家 時間: 2025-3-28 17:18 作者: GUMP 時間: 2025-3-28 19:38
Towards Reverse-Engineering Black-Box Neural Networks. We further show how the exposed internals can be exploited to strengthen adversarial examples against the model. Our work starts an important discussion on the security implications of diagnosing deployed models with limited accessibility. The code is available at ..作者: 配偶 時間: 2025-3-28 23:42
Book 2019ld of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner...The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of?interpretable and explainable AI and AI techniques that have been proposed recently reflecti作者: 把…比做 時間: 2025-3-29 04:24
0302-9743 .The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of?interpretable and explainable AI and AI techniques that have been proposed recently reflecti978-3-030-28953-9978-3-030-28954-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Glutinous 時間: 2025-3-29 10:57 作者: HILAR 時間: 2025-3-29 14:33
https://doi.org/10.1007/978-3-030-28954-6artificial intelligence; computer vision; deep Learning; explainable AI; explanation Methods; fuzzy contr作者: indubitable 時間: 2025-3-29 19:22 作者: 戲服 時間: 2025-3-29 20:42
Interpretability in Intelligent Systems – A New Concept?mples from this legacy that could enrich current interpretability work: First, . were we point to the rich set of ideas developed in the ‘explainable expert systems’ field and, second, tools for . of high-dimensional feature importance maps which have been developed in the field of computational neuroimaging.作者: Nebulizer 時間: 2025-3-30 00:36 作者: harbinger 時間: 2025-3-30 06:35
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/319285.jpg作者: LATHE 時間: 2025-3-30 08:59
978-3-030-28953-9Springer Nature Switzerland AG 2019作者: 開始從未 時間: 2025-3-30 12:31
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning978-3-030-28954-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
