標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer [打印本頁] 作者: 根深蒂固 時(shí)間: 2025-3-21 18:57
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track影響因子(影響力)學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網(wǎng)絡(luò)公開度
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track被引頻次學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track年度引用學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋
書目名稱Machine Learning and Knowledge Discovery in Databases. Research Track讀者反饋學(xué)科排名
作者: PAN 時(shí)間: 2025-3-21 20:33 作者: Processes 時(shí)間: 2025-3-22 02:36
Paolo Bonetti,Alberto Maria Metelli,Marcello Restellirojects and their effectiveness, including the differential impact based on type of aid. This volume is the first of its kind to unpack aid as a complex rather than a unitary concept and explore the wide areas 978-3-030-22123-2978-3-030-22121-8作者: 說不出 時(shí)間: 2025-3-22 05:39
Victor Quétu,Zhu Liao,Enzo Tartaglionerojects and their effectiveness, including the differential impact based on type of aid. This volume is the first of its kind to unpack aid as a complex rather than a unitary concept and explore the wide areas 978-3-030-22123-2978-3-030-22121-8作者: 重疊 時(shí)間: 2025-3-22 10:30
Nikolaos Karalis,Alexander Bigerl,Caglar Demir,Liss Heidrich,Axel-Cyrille Ngonga Ngomo作者: commune 時(shí)間: 2025-3-22 16:09 作者: 諂媚于性 時(shí)間: 2025-3-22 18:15 作者: Tinea-Capitis 時(shí)間: 2025-3-23 00:05
HetCAN: A Heterogeneous Graph Cascade Attention Network with?Dual-Level Awareness cascade blocks. Each cascade block includes two components, the type-aware encoder and the dimension-aware encoder. Specifically, the type-aware encoder compensates for the loss of node type information and aims to make full use of graph heterogeneity. The dimension-aware encoder is able to learn t作者: 無辜 時(shí)間: 2025-3-23 01:27 作者: 有偏見 時(shí)間: 2025-3-23 06:23
Towards Few-Shot Self-explaining Graph Neural Networks the . mimics the decision-making process, which makes predictions based on the generated explanation. Moreover, with a novel meta-training process and a designed mechanism that exploits task information, MSE-GNN can achieve remarkable performance on new few-shot tasks. Extensive experimental result作者: 火車車輪 時(shí)間: 2025-3-23 12:59 作者: Contort 時(shí)間: 2025-3-23 14:57
Self-supervised Spatial-Temporal Normality Learning for?Time Series Anomaly Detectionporal representations for the normal patterns hidden in the time series data. Extensive experiments on five popular TSAD benchmarks show that STEN substantially outperforms state-of-the-art competing methods. Our code is available at ..作者: Anonymous 時(shí)間: 2025-3-23 18:58 作者: Magisterial 時(shí)間: 2025-3-23 22:46
Secure Aggregation Is Not Private Against Membership Inference Attacksl that, contrary to prevailing claims, SecAgg offers weak privacy against membership inference attacks even in a single training round. Indeed, it is difficult to hide a local update by adding other independent local updates when the updates are of high dimension. Our findings underscore the imperat作者: BUOY 時(shí)間: 2025-3-24 03:44
Evaluating Negation with?Multi-way Joins Accelerates Class Expression Learningive evaluation show that our approach outperforms its competition across all datasets and that it is the only one able to scale to large datasets. With our approach, we enable learning algorithms to retrieve information from Web-scale knowledge graphs, hence making ante-hoc explainable machine learn作者: Pert敏捷 時(shí)間: 2025-3-24 07:05
LayeredLiNGAM: A Practical and?Fast Method for?Learning a?Linear Non-gaussian Structural Equation Moumber of variables by . and the number of detected layers by .. Furthermore, . is the computational complexity required to compute independence between two variables. Experimental results show that LayeredLiNGAM is faster than DirectLiNGAM without quality degradation of learned DAGs on synthetic and作者: 走路左晃右晃 時(shí)間: 2025-3-24 14:32 作者: sultry 時(shí)間: 2025-3-24 18:49
Enhancing LLM’s Reliability by?Iterative Verification Attributions with?Keyword Frontingion quality, we design a verification-based iterative optimization algorithm, which continuously updates candidate statements and citations until it produces a satisfactory output result. Experiments on three public knowledge-intensive datasets demonstrate that the proposed framework significantly i作者: 狂熱語言 時(shí)間: 2025-3-24 19:55 作者: concert 時(shí)間: 2025-3-24 23:17 作者: 軍械庫 時(shí)間: 2025-3-25 06:25 作者: Lamina 時(shí)間: 2025-3-25 08:46
Richard Serrano,Charlotte Laclau,Baptiste Jeudy,Christine Largeronent has gone largely unheralded in the U.S. media. A longtime journalist, Warne now captures the insatiability of these industries and the magic of the mangroves. His vivid account will make every reader pause before ordering the shrimp..978-1-61091-024-8作者: 抓住他投降 時(shí)間: 2025-3-25 12:20 作者: Complement 時(shí)間: 2025-3-25 16:11 作者: 萬神殿 時(shí)間: 2025-3-25 20:19
0302-9743 k:?.The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X..978-3-031-70364-5978-3-031-70365-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Root494 時(shí)間: 2025-3-26 03:47
Machine Learning and Knowledge Discovery in Databases. Research TrackEuropean Conference,作者: epidermis 時(shí)間: 2025-3-26 08:16 作者: 玩笑 時(shí)間: 2025-3-26 09:30
Boci Peng,Yongchao Liu,Xiaohe Bo,Sheng Tian,Baokun Wang,Chuntao Hong,Yan Zhangings, with a focus on donor characteristics, political motives, and an evaluation of aid projects and their effectiveness, including the differential impact based on type of aid. This volume is the first of its kind to unpack aid as a complex rather than a unitary concept and explore the wide areas 作者: concubine 時(shí)間: 2025-3-26 16:00
Jingyu Peng,Qi Liu,Linan Yue,Zaixi Zhang,Kai Zhang,Yunhao Sha(Grover and Saeed, 2004). In terms of web usage, critical independent variables are those of location, gender and age. Where the first of these is concerned, the percentage of the population using the Internet varies enormously from continent to continent, as can be seen in Table 1.作者: FOLD 時(shí)間: 2025-3-26 20:21
George Panagopoulos,Daniele Malitesta,Fragkiskos D. Malliaros,Jun Pang(Grover and Saeed, 2004). In terms of web usage, critical independent variables are those of location, gender and age. Where the first of these is concerned, the percentage of the population using the Internet varies enormously from continent to continent, as can be seen in Table 1.作者: 粗語 時(shí)間: 2025-3-26 23:47
Yutong Chen,Hongzuo Xu,Guansong Pang,Hezhe Qiao,Yuan Zhou,Mingsheng Shang(Grover and Saeed, 2004). In terms of web usage, critical independent variables are those of location, gender and age. Where the first of these is concerned, the percentage of the population using the Internet varies enormously from continent to continent, as can be seen in Table 1.作者: Biguanides 時(shí)間: 2025-3-27 01:28
Ashish Kumar,Durga Toshniwal(Grover and Saeed, 2004). In terms of web usage, critical independent variables are those of location, gender and age. Where the first of these is concerned, the percentage of the population using the Internet varies enormously from continent to continent, as can be seen in Table 1.作者: 發(fā)起 時(shí)間: 2025-3-27 06:41 作者: Extricate 時(shí)間: 2025-3-27 13:01
A. V. Arun Kumar,Alistair Shilton,Sunil Gupta,Santu Rana,Stewart Greenhill,Svetha Venkatesh algebra. So the pupils had to leave their highly polished contemporary methods to venture into interpretation. Fortunately, it is a matter of mathematics and this language remains understandable over the centuries. This chapter is an invitation to experience how the light can be born from obscurity作者: buoyant 時(shí)間: 2025-3-27 16:47 作者: 干旱 時(shí)間: 2025-3-27 18:32 作者: 職業(yè)拳擊手 時(shí)間: 2025-3-28 01:12
Conference proceedings 2024om this track, were selected from 30 submissions.?These papers are present in the following volume: Part VIII...?..Applied Data Science Track:?.The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X..作者: forebear 時(shí)間: 2025-3-28 04:37 作者: OTTER 時(shí)間: 2025-3-28 08:39 作者: facetious 時(shí)間: 2025-3-28 11:11 作者: Lymphocyte 時(shí)間: 2025-3-28 15:00
Rejection Ensembles with?Online Calibrationnt. One promising approach for optimizing resource consumption is rejection ensembles. Rejection ensembles combine a small model deployed to an edge device with a large model deployed in the cloud with a rejector tasked to determine the most suitable model for a given input. Due to its novelty, exis作者: 白楊 時(shí)間: 2025-3-28 19:33 作者: 旅行路線 時(shí)間: 2025-3-28 23:01 作者: hemorrhage 時(shí)間: 2025-3-29 04:16 作者: 策略 時(shí)間: 2025-3-29 08:46
Interpetable Target-Feature Aggregation for?Multi-task Learning Based on?Bias-Variance Analysisformance. Previous works have proposed approaches to MTL that can be divided into feature learning, focused on the identification of a common feature representation, and task clustering, where similar tasks are grouped together. In this paper, we propose an MTL approach at the intersection between t作者: ALB 時(shí)間: 2025-3-29 14:46
The Simpler The Better: An Entropy-Based Importance Metric to?Reduce Neural Networks’ Depthmpler downstream tasks, which do not necessarily require a large model’s complexity. Motivated by the awareness of the ever-growing AI environmental impact, we propose an efficiency strategy that leverages prior knowledge transferred by large models. Simple but effective, we propose a method relying作者: 使糾纏 時(shí)間: 2025-3-29 16:04
Towards Few-Shot Self-explaining Graph Neural Networksy in critical domains such as medicine. A promising approach is the self-explaining method, which outputs explanations along with predictions. However, existing self-explaining models require a large amount of training data, rendering them unavailable in few-shot scenarios. To address this challenge作者: 條約 時(shí)間: 2025-3-29 23:04 作者: Germinate 時(shí)間: 2025-3-30 02:42 作者: 格言 時(shí)間: 2025-3-30 05:52 作者: 腐敗 時(shí)間: 2025-3-30 08:58 作者: 河流 時(shí)間: 2025-3-30 14:15 作者: Indebted 時(shí)間: 2025-3-30 20:07
LayeredLiNGAM: A Practical and?Fast Method for?Learning a?Linear Non-gaussian Structural Equation Mol (LiNGAM) is a type of SEM mainly assuming that the graph is a directed acyclic graph (DAG), the relationships are linear, and the noises follow non-Gaussian distributions. DirectLiNGAM is a popular LiNGAM learning method with applications in various fields. However, DirectLiNGAM has computational 作者: NAG 時(shí)間: 2025-3-30 21:56
Enhanced Bayesian Optimization via?Preferential Modeling of?Abstract Properties tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and could greatly benefit from the expertise of its human (domain) experts who often reason abo作者: 使出神 時(shí)間: 2025-3-31 04:47
Enhancing LLM’s Reliability by?Iterative Verification Attributions with?Keyword Frontinglity of large language models (LLMs). However, existing research often ignores the adverse effect of “Middle Loss” in lengthy input contexts on answer correctness, and the potential negative impact of unverified citations on the quality of attribution. To address these challenges, we propose a frame作者: anesthesia 時(shí)間: 2025-3-31 07:45
Reconstructing the?Unseen: GRIOT for?Attributed Graph Imputation with?Optimal Transport attributed graphs. Nevertheless, to work, these methods assume that the attributes values are fully known, which is not realistic in numerous real-world applications. This paper explores the potential of Optimal Transport (OT) to impute missing attributes on graphs. To proceed, we design a novel mu作者: 淡紫色花 時(shí)間: 2025-3-31 13:03 作者: 顛簸下上 時(shí)間: 2025-3-31 14:09 作者: 善變 時(shí)間: 2025-3-31 20:50
Sebastian Buschj?gertiveness.Breaks down the various types of aid and provides eA response to the pressing need to address and clarify the substantial ambiguity within current literature, this edited volume aims to deepen readers’ understanding of the impact of foreign aid on development outcomes based on the latest fi作者: 鬧劇 時(shí)間: 2025-4-1 00:12
Eduardo Fernandes Montesuma,Fred Ngolè Mboula,Antoine Souloumiac understanding of the impact of foreign aid on development outcomes based on the latest findings in research over the past decade. Foreign aid has long been seen as one of two extremes: either beneficial or damaging, a blessing or a curse. Consequently, many readers perceive aid’s effectiveness base作者: 凈禮 時(shí)間: 2025-4-1 02:12
Boci Peng,Yongchao Liu,Xiaohe Bo,Sheng Tian,Baokun Wang,Chuntao Hong,Yan Zhang understanding of the impact of foreign aid on development outcomes based on the latest findings in research over the past decade. Foreign aid has long been seen as one of two extremes: either beneficial or damaging, a blessing or a curse. Consequently, many readers perceive aid’s effectiveness base作者: 易怒 時(shí)間: 2025-4-1 07:28
Zeyuan Zhao,Qingqing Ge,Anfeng Cheng,Yiding Liu,Xiang Li,Shuaiqiang Wangtiveness.Breaks down the various types of aid and provides eA response to the pressing need to address and clarify the substantial ambiguity within current literature, this edited volume aims to deepen readers’ understanding of the impact of foreign aid on development outcomes based on the latest fi作者: NAIVE 時(shí)間: 2025-4-1 12:16 作者: 熄滅 時(shí)間: 2025-4-1 16:33 作者: Annotate 時(shí)間: 2025-4-1 20:18
