標(biāo)題: Titlebook: Engineering Dependable and Secure Machine Learning Systems; Third International Onn Shehory,Eitan Farchi,Guy Barash Conference proceedings [打印本頁(yè)] 作者: Coronary-Artery 時(shí)間: 2025-3-21 19:36
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems影響因子(影響力)
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems被引頻次
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems被引頻次學(xué)科排名
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems年度引用
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems年度引用學(xué)科排名
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems讀者反饋
書(shū)目名稱(chēng)Engineering Dependable and Secure Machine Learning Systems讀者反饋學(xué)科排名
作者: 愚蠢人 時(shí)間: 2025-3-21 21:18 作者: Choreography 時(shí)間: 2025-3-22 03:55
,Konstruktion l?rmarmer Maschinen,ency information. We show that on all quantitative and qualitative evaluations, the combined model gives the best results, but also that only training with RL and without any syntactic information already gives nearly as good results as syntax-aware models with less parameters and faster training convergence.作者: inflame 時(shí)間: 2025-3-22 08:34
Learner-Independent Targeted Data Omission Attacks, this effectiveness via a series of attack experiments against various learning mechanisms. We show that, with a relatively low attack budget, our omission attack succeeds regardless of the target learner.作者: Ischemic-Stroke 時(shí)間: 2025-3-22 08:48 作者: 使殘廢 時(shí)間: 2025-3-22 16:02 作者: 使殘廢 時(shí)間: 2025-3-22 19:14 作者: 偉大 時(shí)間: 2025-3-23 01:06
1865-0929 om 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.?.978-3-030-62143-8978-3-030-62144-5Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: Eviction 時(shí)間: 2025-3-23 04:32
Conference proceedings 2020DSMLS 2020, held in?New York City, NY, USA, in February 2020.?.The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software syste作者: osteopath 時(shí)間: 2025-3-23 07:00
,Thermische oder mechanische überbelastung,ns to the principal components of neural network inputs. We propose a new metric for neural networks to measure their robustness to adversarial samples, termed the (.,?.) point. We utilize this metric to achieve 93.36% accuracy in detecting adversarial samples independent of architecture and attack type for models trained on ImageNet.作者: observatory 時(shí)間: 2025-3-23 13:09
Principal Component Properties of Adversarial Samples,ns to the principal components of neural network inputs. We propose a new metric for neural networks to measure their robustness to adversarial samples, termed the (.,?.) point. We utilize this metric to achieve 93.36% accuracy in detecting adversarial samples independent of architecture and attack type for models trained on ImageNet.作者: 售穴 時(shí)間: 2025-3-23 14:03
1865-0929 Systems, EDSMLS 2020, held in?New York City, NY, USA, in February 2020.?.The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML soft作者: jarring 時(shí)間: 2025-3-23 20:11
Communications in Computer and Information Sciencehttp://image.papertrans.cn/e/image/310749.jpg作者: Obliterate 時(shí)間: 2025-3-24 01:27 作者: 處理 時(shí)間: 2025-3-24 03:42
Neue Entwicklungen und Zukunftsperspektiven, to fool a model, but appear normal to human beings. Recent work has shown that pixel discretization can be used to make classifiers for MNIST highly robust to adversarial examples. However, pixel discretization fails to provide significant protection on more complex datasets. In this paper, we take作者: 兵團(tuán) 時(shí)間: 2025-3-24 06:54
https://doi.org/10.1007/978-3-322-86803-9wever, while poisoning attacks typically corrupt data in various ways including addition, omission and modification, to optimize the attack, we focus on omission only, which is much simpler to implement and analyze. A major advantage of our attack method is its generality. While poisoning attacks ar作者: 清澈 時(shí)間: 2025-3-24 13:32 作者: ascend 時(shí)間: 2025-3-24 15:07 作者: endarterectomy 時(shí)間: 2025-3-24 19:46 作者: Sputum 時(shí)間: 2025-3-25 00:54 作者: LINES 時(shí)間: 2025-3-25 04:55 作者: 熱烈的歡迎 時(shí)間: 2025-3-25 10:00 作者: 煉油廠 時(shí)間: 2025-3-25 13:32 作者: Explicate 時(shí)間: 2025-3-25 18:09
https://doi.org/10.1007/978-3-030-62144-5artificial intelligence; computer networks; computer programming; computer security; computer systems; co作者: 聾子 時(shí)間: 2025-3-25 23:42
978-3-030-62143-8Springer Nature Switzerland AG 2020作者: 弄皺 時(shí)間: 2025-3-26 03:31
Engineering Dependable and Secure Machine Learning Systems978-3-030-62144-5Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 特征 時(shí)間: 2025-3-26 05:03 作者: Albumin 時(shí)間: 2025-3-26 09:36 作者: 語(yǔ)言學(xué) 時(shí)間: 2025-3-26 15:42 作者: DAMP 時(shí)間: 2025-3-26 18:25
Extraction of Complex DNN Models: Real Threat or Boogeyman?,ing intellectual property of ML models has emerged as an important consideration. Confidentiality of ML models can be protected by exposing them to clients only via prediction APIs. However, model extraction attacks can steal the functionality of ML models using the information leaked to clients thr作者: 變形 時(shí)間: 2025-3-27 00:05
Principal Component Properties of Adversarial Samples,a benign image that can easily fool trained neural networks, posing a significant risk to their commercial deployment. In this work, we analyze adversarial samples through the lens of their contributions to the principal components of . image, which is different than prior works in which authors per作者: 雄偉 時(shí)間: 2025-3-27 02:09 作者: Forage飼料 時(shí)間: 2025-3-27 08:50
Density Estimation in Representation Space to Predict Model Uncertainty,ir training dataset. We propose a novel and straightforward approach to estimate prediction uncertainty in a pre-trained neural network model. Our method estimates the training data density in representation space for a novel input. A neural network model then uses this information to determine whet作者: Glaci冰 時(shí)間: 2025-3-27 11:14
Automated Detection of Drift in Deep Learning Based Classifiers Performance Using Network Embeddingly sampled test set is used to estimate the performance (e.g., accuracy) of the neural network during deployment time. The performance on the test set is used to project the performance of the neural network at deployment time under the implicit assumption that the data distribution of the test set 作者: occult 時(shí)間: 2025-3-27 16:41 作者: squander 時(shí)間: 2025-3-27 19:29
Dependable Neural Networks for Safety Critical Tasks, perform safely in novel scenarios. It is challenging to verify neural networks because their decisions are not explainable, they cannot be exhaustively tested, and finite test samples cannot capture the variation across all operating conditions. Existing work seeks to train models robust to new sce作者: minaret 時(shí)間: 2025-3-27 22:21 作者: SIT 時(shí)間: 2025-3-28 05:32
Neue Entwicklungen und Zukunftsperspektiven,TSRB and MS-COCO. Our initial results suggest that using attention mask leads to improved robustness. On the adversarially trained classifiers, we see an adversarial robustness increase of over 20% on MS-COCO.作者: ENACT 時(shí)間: 2025-3-28 07:59 作者: 手工藝品 時(shí)間: 2025-3-28 11:58
Technischer Lehrgang: Hydraulische Systemeerformance assessment. Here we demonstrate a novel technique, called IBM FreaAI, which automatically extracts explainable feature slices for which the ML solution’s performance is statistically significantly worse than the average. We demonstrate results of evaluating ML classifier models on seven o作者: 明智的人 時(shí)間: 2025-3-28 18:34 作者: 提名 時(shí)間: 2025-3-28 22:39 作者: concise 時(shí)間: 2025-3-29 02:03
