標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p [打印本頁] 作者: Bunion 時間: 2025-3-21 16:24
書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)
書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度
書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases年度引用
書目名稱Machine Learning and Knowledge Discovery in Databases年度引用學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋
書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名
作者: 一再困擾 時間: 2025-3-21 20:48
Foveated Neural Computationional burden. FCLs can be stacked into neural architectures and we evaluate them in several tasks, showing how they efficiently handle the information in the peripheral regions, eventually avoiding the development of misleading biases. When integrated with a model of human attention, FCL-based netwo作者: anarchist 時間: 2025-3-22 01:12
Trigger Detection for?the?sPHENIX Experiment via?Bipartite Graph Networks with?Set Transformerits importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our mo作者: Infuriate 時間: 2025-3-22 07:13
Understanding Difficulty-Based Sample Weighting with?a?Universal Difficulty Measuresed as a universal difficulty measure. Furthermore, we provide formal theoretical justifications on the role of difficulty-based weighting for deep learning, consequently revealing its positive influences on both the optimization dynamics and generalization performance of deep models, which is instr作者: 傳染 時間: 2025-3-22 11:44
Avoiding Forgetting and?Allowing Forward Transfer in?Continual Learning via?Sparse NetworksL using fix-capacity models. AFAF allocates a sub-network that enables . transfer of relevant knowledge to a new task while preserving past knowledge, . some of the previously allocated components to utilize the fixed-capacity, and addressing class-ambiguities when similarities exist. The experiment作者: LIKEN 時間: 2025-3-22 13:15
PrUE: Distilling Knowledge from?Sparse Teacher Networkseffectiveness of the proposed method with experiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet. Results indicate that student networks trained with sparse teachers achieve better performance. Besides, our method allows researchers to distill knowledge from deeper networks to improve students fur作者: Inscrutable 時間: 2025-3-22 18:46
FROB: Few-Shot ROBust Model for?Joint Classification and?Out-of-Distribution Detectiondology for sample generation on the normal class distribution confidence boundary based on generative and discriminative models, including classification. FROB implicitly generates adversarial samples, and forces samples from OoD, including our boundary, to be less confident by the classifier. By in作者: 聯(lián)想 時間: 2025-3-23 00:24
PRoA: A Probabilistic Robustness Assessment Against Functional Perturbationsbilistic robustness of a model, ., the probability of failure encountered by the trained model after deployment. Our experiments demonstrate the effectiveness and flexibility of PRoA in terms of evaluating the probabilistic robustness against a broad range of functional perturbations, and PRoA can s作者: 可轉(zhuǎn)變 時間: 2025-3-23 04:50
Hypothesis Testing for?Class-Conditional Label Noiseor is approximately 1/2. The proposed hypothesis tests are built upon the asymptotic properties of Maximum Likelihood Estimators for Logistic Regression models. We establish the main properties of the tests, including a theoretical and empirical analysis of the dependence of the power on the test on作者: 不溶解 時間: 2025-3-23 05:37 作者: Cholesterol 時間: 2025-3-23 11:53 作者: 我們的面粉 時間: 2025-3-23 14:02
Conference proceedings 2023rative models; computer vision; meta-learning, neural architecture search; ..Part IV:. Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; ...Part V:. Supervised learning; probabilistic inferenc作者: 相一致 時間: 2025-3-23 20:47 作者: 撕裂皮肉 時間: 2025-3-23 22:57 作者: constitute 時間: 2025-3-24 02:51
Zhihao Zhu,Chenwang Wu,Min Zhou,Hao Liao,Defu Lian,Enhong Chen作者: FAR 時間: 2025-3-24 06:56 作者: Canopy 時間: 2025-3-24 13:44
onsanpassung und Innovationsf?rderung reduziert wurde. Diese Tendenz ist nicht nur bei Arbeitgebern (z.B. Kuratorium der deutschen Wirtschaft für Berufsbildung), Bundesregierung und CDU/CSU/FDP (vgl. BMWi), sondern z.T. auch bei Gewerkschaften, SPD und Grünen zu beobachten..作者: Peak-Bone-Mass 時間: 2025-3-24 17:57 作者: colostrum 時間: 2025-3-24 22:55
Zhenhe Wu,Liangqing Wu,Shuangyong Song,Jiahao Ji,Bo Zou,Zhoujun Li,Xiaodong Hetiert. Pers?nlichkeitsentwicklung und St?rkung der Eigenverantwortung sind neben der Wissensvermittlung wichtige Ziele, an denen sich die Bildung orientieren mu?. Diese Forderung bezieht sich nicht nur auf die akademische Ausbildung ... Notwendig sind eine gr??ere Flexibilit?t und Durchl?ssigkeit so作者: Cardioversion 時間: 2025-3-25 02:15 作者: 吃掉 時間: 2025-3-25 03:24
Marcus de Carvalho,Mahardhika Pratama,Jie Zhang,Yajuan Sun im Sinne des liberalen Paternalismus Menschen dazu bewegt werden k?nnen, die richtigen Entscheidungen zu treffen und ihr Verhalten zu ?ndern, z.?B. umweltbewusster zu handeln. Das vorliegende Forschungskonzept forciert nachhaltiges Verhalten von Fernstudierenden. Diese sollen durch Nudges motiviert作者: 減弱不好 時間: 2025-3-25 09:10
Tingting Xuan,Giorgian Borca-Tasciuc,Yimin Zhu,Yu Sun,Cameron Dean,Zhaozhong Shi,Dantong Yuspiel die Beforschung von Lehre, die versucht Studierende für Engagement zu gewinnen. Für den Bereich Transfer k?nnte z.?B. ein Angebot von Schulungen zur Klimakommunikation für lokale Akteure interessant sein. Für das Campusmanagement scheint die Entwicklung einer geeigneten Infrastruktur zentral, 作者: 水獺 時間: 2025-3-25 13:31
Xiaoling Zhou,Ou Wu,Weiyao Zhu,Ziyang Liangnn die Hausaufgaben als integrierter Bestandteil des Technikunterrichtes angesehen werden. Integriert bedeutet hier auch, da? in den Hausaufgaben die gleichen Kommunikationsformen angesprochen werden wie im Unterricht, so da? es den Schülern eher m?glich ist, ihre Lernfortschritte selbst zu erkennen作者: 誰在削木頭 時間: 2025-3-25 19:12
Ghada Sokar,Decebal Constantin Mocanu,Mykola Pechenizkiynn die Hausaufgaben als integrierter Bestandteil des Technikunterrichtes angesehen werden. Integriert bedeutet hier auch, da? in den Hausaufgaben die gleichen Kommunikationsformen angesprochen werden wie im Unterricht, so da? es den Schülern eher m?glich ist, ihre Lernfortschritte selbst zu erkennen作者: ALOFT 時間: 2025-3-25 20:18
Shaopu Wang,Xiaojun Chen,Mengzhen Kou,Jinqiao Shinn die Hausaufgaben als integrierter Bestandteil des Technikunterrichtes angesehen werden. Integriert bedeutet hier auch, da? in den Hausaufgaben die gleichen Kommunikationsformen angesprochen werden wie im Unterricht, so da? es den Schülern eher m?glich ist, ihre Lernfortschritte selbst zu erkennen作者: OVER 時間: 2025-3-26 04:07 作者: Hot-Flash 時間: 2025-3-26 06:43 作者: 不朽中國 時間: 2025-3-26 11:05 作者: 詼諧 時間: 2025-3-26 15:13 作者: 類人猿 時間: 2025-3-26 17:06
Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka作者: 享樂主義者 時間: 2025-3-26 21:51
0302-9743 e Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022..The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions...The volumes 作者: Processes 時間: 2025-3-27 01:59
Class-Incremental Learning via?Knowledge Amalgamation need to share the same network structure, allowing heterogeneous tasks to be adapted to a compact or sparse data representation. We compare our method with competitive baselines from different strategies, demonstrating our approach’s advantages. Source-code: ..作者: Amplify 時間: 2025-3-27 08:27 作者: 起波瀾 時間: 2025-3-27 10:10 作者: armistice 時間: 2025-3-27 15:43 作者: Ischemia 時間: 2025-3-27 20:13
Conference proceedings 2023y in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022..The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions...The volumes are organi作者: NAUT 時間: 2025-3-27 23:42 作者: 臭了生氣 時間: 2025-3-28 05:12
Calibrating Distance Metrics Under Uncertaintyze the calibration process. The experimental results from a series of empirical evaluations justified the benefits of the proposed approach and demonstrated its high potential in practical applications.作者: 暗語 時間: 2025-3-28 07:22 作者: 有說服力 時間: 2025-3-28 12:24 作者: FLAT 時間: 2025-3-28 17:51
Class-Incremental Learning via?Knowledge Amalgamations methods have been proposed to address the catastrophic forgetting problem where an agent loses its generalization power of old tasks while learning new tasks. We put forward an alternative strategy to handle the catastrophic forgetting with knowledge amalgamation (CFA), which learns a student netw作者: Agility 時間: 2025-3-28 22:46
Trigger Detection for?the?sPHENIX Experiment via?Bipartite Graph Networks with?Set Transformerlso plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes作者: Stable-Angina 時間: 2025-3-29 02:46
Understanding Difficulty-Based Sample Weighting with?a?Universal Difficulty Measureto calculate their weights. In this study, this scheme is called difficulty-based weighting. Two important issues arise when explaining this scheme. First, a unified difficulty measure that can be theoretically guaranteed for training samples does not exist. The learning difficulties of the samples 作者: murmur 時間: 2025-3-29 03:37
Avoiding Forgetting and?Allowing Forward Transfer in?Continual Learning via?Sparse Networksmodels without access to past data. Current methods focus only on selecting a sub-network for a new task that reduces forgetting of past tasks. However, this selection could limit the forward transfer of . past knowledge that helps in future learning. Our study reveals that satisfying both objective作者: Medicare 時間: 2025-3-29 09:47
PrUE: Distilling Knowledge from?Sparse Teacher Networkshead on deployment. To compress these models, knowledge distillation was proposed to transfer knowledge from a cumbersome (teacher) network into a lightweight (student) network. However, guidance from a teacher does not always improve the generalization of students, especially when the size gap betw作者: Interstellar 時間: 2025-3-29 12:05
Fooling Partial Dependence via?Data Poisoningut that such explanations are not robust nor trustworthy, and they can be fooled. This paper presents techniques for attacking Partial Dependence (plots, profiles, PDP), which are among the most popular methods of explaining any predictive model trained on tabular data. We showcase that PD can be ma作者: ACME 時間: 2025-3-29 18:15 作者: 顯而易見 時間: 2025-3-29 23:26 作者: 健忘癥 時間: 2025-3-30 00:37
Hypothesis Testing for?Class-Conditional Label Noiseactitioner already has preconceptions on possible distortions that may have affected the labels, which allow us to pose the task as the design of hypothesis tests. As a first approach, we focus on scenarios where a given dataset of instance-label pairs has been corrupted with ., as opposed to ., wit作者: Fortify 時間: 2025-3-30 06:59
On the?Prediction Instability of?Graph Neural Networksst in machine learning systems. In this paper, we systematically assess the prediction instability of node classification with state-of-the-art Graph Neural Networks (GNNs). With our experiments, we establish that multiple instantiations of popular GNN models trained on the same data with the same m作者: 沒花的是打擾 時間: 2025-3-30 09:42 作者: Schlemms-Canal 時間: 2025-3-30 15:14 作者: 變化無常 時間: 2025-3-30 17:28
Defending Observation Attacks in?Deep Reinforcement Learning via?Detection and?Denoisingsider attacks manifesting as perturbations in the observation space managed by the external environment. These attacks have been shown to downgrade policy performance significantly. We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuo作者: 無情 時間: 2025-3-30 22:30 作者: textile 時間: 2025-3-31 03:29 作者: strain 時間: 2025-3-31 06:25 作者: grieve 時間: 2025-3-31 09:32 作者: 托人看管 時間: 2025-3-31 16:16 作者: Incise 時間: 2025-3-31 18:11
Matteo Tiezzi,Simone Marullo,Alessandro Betti,Enrico Meloni,Lapo Faggi,Marco Gori,Stefano Melaccin Widerwillen gegen die Schule nach Hause bringt.“ So beschrieb Leo Tolstoi nach einer Reise durch Deutschland in seinen ?Gedanken über Volksbildung“ im Jahre 1861 die deutsche Bildungsszene. Die Weiterbildung war damals kein Thema. Aber w?re dies — vorausgesetzt, man würde ?Schule“ durch ?Weiterbil作者: Maximizer 時間: 2025-4-1 00:09
