標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2021; 30th International C Igor Farka?,Paolo Masulli,Stefan Wermter Conference proc [打印本頁(yè)] 作者: 廚房默契 時(shí)間: 2025-3-21 17:46
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021影響因子(影響力)
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021被引頻次
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021被引頻次學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021年度引用
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021年度引用學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021讀者反饋
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2021讀者反饋學(xué)科排名
作者: agonist 時(shí)間: 2025-3-22 00:12 作者: Influx 時(shí)間: 2025-3-22 00:49
How to Compare Adversarial Robustness of Classifiers from a Global Perspectivey of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a given model. Point-wise measures for specific threat models are currently the most popular tool for comparing the robustness of classi作者: 心神不寧 時(shí)間: 2025-3-22 08:05 作者: Manifest 時(shí)間: 2025-3-22 12:37 作者: Spinal-Fusion 時(shí)間: 2025-3-22 13:27 作者: cutlery 時(shí)間: 2025-3-22 17:46
Statistical Certification of Acceptable Robustness for Neural Networksrk verification and validation, do not fully meet our criteria for robustness measurement. From the industrial point-of-view, this paper proposes to use statistical robustness certificates (SRC) for measuring the robustness of neural networks against random noises as well as semantic perturbations a作者: 滑稽 時(shí)間: 2025-3-22 21:22 作者: 流逝 時(shí)間: 2025-3-23 03:24
CmaGraph: A TriBlocks Anomaly Detection Method in Dynamic Graph Using Evolutionary Community Represee accurate community structures in a dynamic graph. This paper introduces CmaGraph, a TriBlocks framework using an innovative deep metric learning block to measure the distances between vertices within and between communities from an evolution community detection block. A one-class anomaly detection作者: 變態(tài) 時(shí)間: 2025-3-23 06:22 作者: corn732 時(shí)間: 2025-3-23 10:44
Attention-Based Bi-LSTM for Anomaly Detection on Time-Series Dataundamental aspect of developing intelligent automated systems. Existing work in this field has primarily focused on developing intelligent systems that use dimensionality reduction or regression-based approaches to annotate data based on a certain static threshold. Researchers in fields such as Natu作者: GRACE 時(shí)間: 2025-3-23 17:04 作者: 庇護(hù) 時(shí)間: 2025-3-23 18:24
Feature Creation Towards the Detection of Non-control-Flow Hijacking Attacksis achieved by using hardware event counts as features to describe the behavior of the software program. Then a classifier, such as support vector machine (SVM) or neural network, can be used to detect the anomalous behavior caused by malware attacks. The collected datasets to describe the program b作者: DRAFT 時(shí)間: 2025-3-23 23:29
An Attention Module for Convolutional Neural Networksor convolutional neural networks. However, we found two ignored problems in current attentional activations-based models: the approximation problem and the insufficient capacity problem of the attention maps. To solve the two problems together, we initially propose an attention module for convolutio作者: 指耕作 時(shí)間: 2025-3-24 04:36
Attention-Based 3D Neural Architectures for Predicting Cracks in Designsication of parts used in critical industrial applications. This paper presents promising outcomes from applying attention-based neural architectures for predicting such 3D stress phenomena accurately, efficiently, and reliably. This capability is critical to drastically reducing the design maturatio作者: 瑪瑙 時(shí)間: 2025-3-24 10:08
Entity-Aware Biaffine Attention for?Constituent Parsing models have achieved state-of-the-art results on this task, few consider entity-violating issue, i.e. an entity cannot form a complete sub-tree in the resultant constituent parsing tree. To attack this issue, this paper proposes an entity-aware biaffine attention model for constituent parsing. It l作者: 易彎曲 時(shí)間: 2025-3-24 14:45
Fertigungstechnik mit Kleb- und Dichtstoffenfor. In this paper, we introduce new and improved reprogramming technique that, compared to prior works, achieves better accuracy, scalability, and can be successfully applied to more complex tasks. While prior literature focuses on potential malicious uses of reprogramming, we argue that reprogramm作者: Hearten 時(shí)間: 2025-3-24 14:53 作者: 加強(qiáng)防衛(wèi) 時(shí)間: 2025-3-24 22:59
https://doi.org/10.1007/978-3-642-92956-4y of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a given model. Point-wise measures for specific threat models are currently the most popular tool for comparing the robustness of classi作者: 排出 時(shí)間: 2025-3-24 23:15 作者: Ambulatory 時(shí)間: 2025-3-25 03:38 作者: alleviate 時(shí)間: 2025-3-25 08:19
https://doi.org/10.1007/978-3-658-18300-4ded a strong baseline for GEC and achieved excellent results by fine-tuning on a small amount of annotated data. However, due to the lack of large-scale erroneous-corrected parallel datasets, these models tend to suffer from the problem of overfitting. Previous researchers have proposed a variety of作者: PATRI 時(shí)間: 2025-3-25 11:43 作者: characteristic 時(shí)間: 2025-3-25 16:13 作者: 我邪惡 時(shí)間: 2025-3-25 20:26
Rundlauffehler und Spannmittelkonstruktion,e accurate community structures in a dynamic graph. This paper introduces CmaGraph, a TriBlocks framework using an innovative deep metric learning block to measure the distances between vertices within and between communities from an evolution community detection block. A one-class anomaly detection作者: 工作 時(shí)間: 2025-3-26 03:56 作者: EXALT 時(shí)間: 2025-3-26 07:46 作者: Oversee 時(shí)間: 2025-3-26 10:29
Wilfried K?nig VDI,Fritz Klocke VDIta’s strong expression ability. However, at present, graph-based methods mainly focus on node-level anomaly detection, while edge-level anomaly detection is relatively minor. Anomaly detection at the edge level can distinguish the specific edges connected to nodes as detection objects, so its resolu作者: 按時(shí)間順序 時(shí)間: 2025-3-26 14:24 作者: 改革運(yùn)動(dòng) 時(shí)間: 2025-3-26 19:59 作者: 條約 時(shí)間: 2025-3-26 22:46
Verfahren mit rotatorischer Hauptbewegung,ication of parts used in critical industrial applications. This paper presents promising outcomes from applying attention-based neural architectures for predicting such 3D stress phenomena accurately, efficiently, and reliably. This capability is critical to drastically reducing the design maturatio作者: somnambulism 時(shí)間: 2025-3-27 01:26 作者: ELATE 時(shí)間: 2025-3-27 07:01 作者: 控制 時(shí)間: 2025-3-27 10:53
https://doi.org/10.1007/978-3-030-86362-3artificial intelligence; computer hardware; computer networks; computer vision; data mining; image analys作者: 慢跑鞋 時(shí)間: 2025-3-27 14:14 作者: AGATE 時(shí)間: 2025-3-27 18:04 作者: endure 時(shí)間: 2025-3-27 22:44 作者: 朦朧 時(shí)間: 2025-3-28 04:49 作者: gerrymander 時(shí)間: 2025-3-28 08:13
0302-9743 erence on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes..In this volume, the papers focus on topics such as adv作者: 媒介 時(shí)間: 2025-3-28 13:14
Conference proceedings 2021Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes..In this volume, the papers focus on topics such as adversarial m作者: 別炫耀 時(shí)間: 2025-3-28 18:09 作者: 發(fā)芽 時(shí)間: 2025-3-28 20:08
https://doi.org/10.1007/978-3-322-85610-4ring robustness of different neural networks and has polynomial time complexity which leads to 3x-30x boost in efficiency compared to related methods. Together with the intrinsic statistical guarantee, the issued certificates are considered practical in comparing the robustness of various commercial neural networks.作者: 大門(mén)在匯總 時(shí)間: 2025-3-28 23:03
Rundlauffehler und Spannmittelkonstruktion,anomalous edges by reconstructing the distance between the evolutionary communities’ vertices. We demonstrate the implications on three real-world datasets and compare them with the state-of-the-art method.作者: adjacent 時(shí)間: 2025-3-29 05:54 作者: Haphazard 時(shí)間: 2025-3-29 10:14
An Improved (Adversarial) Reprogramming Technique for?Neural?Networkshem to perform new tasks. This technique requires a lot less effort and hyperparameter tuning compared training new models from scratch. Therefore, we believe that our improved and scalable reprogramming method has potential to become a new method for creating neural network models.作者: 圓柱 時(shí)間: 2025-3-29 12:43
Statistical Certification of Acceptable Robustness for Neural Networksring robustness of different neural networks and has polynomial time complexity which leads to 3x-30x boost in efficiency compared to related methods. Together with the intrinsic statistical guarantee, the issued certificates are considered practical in comparing the robustness of various commercial neural networks.作者: Moderate 時(shí)間: 2025-3-29 17:51 作者: barium-study 時(shí)間: 2025-3-29 22:10 作者: 扔掉掐死你 時(shí)間: 2025-3-30 01:00 作者: AVERT 時(shí)間: 2025-3-30 08:02 作者: Anthem 時(shí)間: 2025-3-30 09:04 作者: apiary 時(shí)間: 2025-3-30 13:07 作者: Lipoprotein(A) 時(shí)間: 2025-3-30 16:49 作者: 經(jīng)典 時(shí)間: 2025-3-30 21:19
https://doi.org/10.1007/978-3-658-18300-4 to enhance the robustness of the model based on adversarial training. This approach constructs the adversarial samples and treats them as the augmented data. Unlike previous methods that introduce token-level noise, our method introduces embedding-level noise and can obtain extra samples that are c作者: 或者發(fā)神韻 時(shí)間: 2025-3-31 02:18
https://doi.org/10.1007/978-3-322-85610-4 are unknown. Then, a surrogate model is trained to have similar functional (i.e. input-output mapping) and switching power characteristics as the oracle (black-box) model. Our results indicate that the inclusion of power consumption data increases the fidelity of the model extraction by up?to 30% b作者: 描述 時(shí)間: 2025-3-31 08:12
Wilfried K?nig VDI,Fritz Klocke VDIenta Anomaly Benchmark (NAB). Additionally, we also contribute by creating new baselines on the NAB with recent models such as REBM, DAGMM, LSTM-ED, and Donut, which have not been previously used on the NAB.作者: 連鎖,連串 時(shí)間: 2025-3-31 12:00
Wilfried K?nig VDI,Fritz Klocke VDIg strategy to train the model on a large-scale graph. It improves the scalability of the model. Second, we design an edge convolutional neural network layer to realize the fusion of edge neighborhood information. We take the reconstruction error as the evaluation criterion after stacking multiple ed作者: Observe 時(shí)間: 2025-3-31 13:51 作者: Germinate 時(shí)間: 2025-3-31 19:27
https://doi.org/10.1007/978-3-662-54207-1effectiveness of our proposed attention module. In particular, our proposed attention module achieves . Top-1 accuracy improvement on ImageNet classification over a ResNet101 baseline and 0.63 COCO-style Average Precision improvement on the COCO object detection on top of a Faster R-CNN baseline wit作者: pellagra 時(shí)間: 2025-3-31 23:01
Verfahren mit rotatorischer Hauptbewegung,n. In response, a Deep Convolutional Neural Network (DCNN) model is explored as a surrogate for the physics-based model, so that it can be used to time-efficiently estimate the crack index for a given part-design. This requires careful design of the training regime and dataset for a given design pro
