標題: 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: STRI 時間: 2025-3-21 19:17
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: 光滑 時間: 2025-3-21 20:55
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620544.jpg作者: SLAY 時間: 2025-3-22 02:44 作者: CRUE 時間: 2025-3-22 06:44 作者: SPURN 時間: 2025-3-22 11:36
Fast Redescription Mining Using Locality-Sensitive Hashing we present new algorithms that perform the matching and extension orders of magnitude faster than the existing approaches. Our algorithms are based on locality-sensitive hashing with a tailored approach to handle the discretisation of numerical attributes as used in redescription mining.作者: predict 時間: 2025-3-22 16:31 作者: figure 時間: 2025-3-22 17:19
Conference proceedings 2024scovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024...?..The papers presented in these proceedings are from the following three conference tracks: -..Research Track:.?The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 su作者: LAIR 時間: 2025-3-22 23:49 作者: emulsify 時間: 2025-3-23 02:47
Model-Based Reinforcement Learning with?Multi-task Offline Pretrainingsferring the task-agnostic knowledge of physical dynamics to facilitate world model training, and (ii) learning to replay relevant source actions to guide the target policy. We demonstrate the advantages of our approach compared with the state-of-the-art methods in Meta-World and DeepMind Control Suite.作者: LIMN 時間: 2025-3-23 05:47 作者: Airtight 時間: 2025-3-23 13:32 作者: 使?jié)M足 時間: 2025-3-23 14:40 作者: abnegate 時間: 2025-3-23 19:03
Ruiqi Xue,Ziqian Zhang,Lihe Li,Feng Chen,Yi-Chen Li,Yang Yu,Lei Yuan作者: glowing 時間: 2025-3-24 00:22 作者: alabaster 時間: 2025-3-24 02:47
Mengyang Chen,Lingwei Wei,Han Cao,Wei Zhou,Zhou Yan,Songlin Hu作者: LANCE 時間: 2025-3-24 07:56
Alexandre Audibert,Aurélien Gauffre,Massih-Reza Amini作者: Peculate 時間: 2025-3-24 14:02 作者: Vaginismus 時間: 2025-3-24 18:18
Denis Huseljic,Paul Hahn,Marek Herde,Lukas Rauch,Bernhard Sick作者: 不足的東西 時間: 2025-3-24 21:21
Machine Learning and Knowledge Discovery in Databases. Research TrackEuropean Conference,作者: Ordeal 時間: 2025-3-25 01:11 作者: 脫離 時間: 2025-3-25 03:25 作者: 污點 時間: 2025-3-25 07:54 作者: 希望 時間: 2025-3-25 11:51
Dynamics Adaptive Safe Reinforcement Learning with?a?Misspecified Simulatortraditional methods. Subsequently, DASaR aligns the estimated value functions in the simulator and the real-world environment via inverse dynamics-based relabeling of reward and cost signals. Furthermore, to deal with the underestimation of cost value functions, DASaR employs uncertainty estimation 作者: 群居男女 時間: 2025-3-25 19:07 作者: consent 時間: 2025-3-25 23:38 作者: crescendo 時間: 2025-3-26 02:54
FairFlow: An Automated Approach to?Model-Based Counterfactual Data Augmentation for NLP paper proposes FairFlow, an automated approach to generating parallel data for training counterfactual text generator models that limits the need for human intervention. Furthermore, we show that FairFlow significantly overcomes the limitations of dictionary-based word-substitution approaches whils作者: 助記 時間: 2025-3-26 05:56 作者: 不要不誠實 時間: 2025-3-26 12:16
MEGA: Multi-encoder GNN Architecture for?Stronger Task Collaboration and?Generalizationng of each task. This architecture allows for independent learning from multiple pretext tasks, followed by a simple self-supervised dimensionality reduction technique to combine the insights gleaned. Through extensive experiments, we demonstrate the superiority of our approach, showcasing an averag作者: Estrogen 時間: 2025-3-26 15:01
MetaQuRe: Meta-learning from?Model Quality and?Resource Consumptionurce consumption of models evaluated across hundreds of data sets and four execution environments. We use this data to put our methodology into practice and conduct an in-depth analysis of how our approach and data set can help in making AutoML more resource-aware, which represents our third contrib作者: Fracture 時間: 2025-3-26 20:03
Propagation Structure-Semantic Transfer Learning for?Robust Fake News Detectiontion under a teacher-student architecture. Specifically, we design dual teacher models to learn semantics knowledge and structure knowledge from noisy news content and propagation structure independently. Besides, we design a Multi-channel Knowledge Distillation (MKD) loss to enable the student mode作者: conceal 時間: 2025-3-27 00:52
Exploring Contrastive Learning for?Long-Tailed Multi-label Text Classificationwe identify two critical challenges associated with contrastive learning: the “l(fā)ack of positives” and the “attraction-repulsion imbalance”. Building on these insights, we introduce a novel contrastive loss function for MLTC. It attains Micro-F1 scores that either match or surpass those obtained with作者: ROOF 時間: 2025-3-27 02:35 作者: 豎琴 時間: 2025-3-27 05:57 作者: Affection 時間: 2025-3-27 12:15 作者: carbohydrate 時間: 2025-3-27 13:38 作者: Opponent 時間: 2025-3-27 19:12
Yurui Lai,Taiyan Zhang,Rui Fanr of developers who can’t program.Avoid the pitfalls of working alone.Who This Book Is For..Anyone who is curious about software development as a career choice. You have zero to five years of software development experience and want to learn non-technical skills that can help your career. ?It is als作者: confide 時間: 2025-3-28 00:17
Maiju Karjalainen,Esther Galbrun,Pauli Miettinenetters to the editor in African-American newspapers. This chapter attempts to undertake two tasks—to explore some of the major themes in the letters in a dozen African-American newspapers over three different time periods, 1929, 1968 and 1972, and, secondly, to call for more such research into the l作者: 內(nèi)部 時間: 2025-3-28 05:41
obably became more bourgeois, despite growing working-class readership, and more splintered, but did not decline. This is the first systematic study of readers’ letters in the mainstream Victorian press (i.e. newspapers produced outside London). Local weekly newspapers have been chosen because they 作者: 辯論的終結(jié) 時間: 2025-3-28 07:29
Ewoenam Kwaku Tokpo,Toon Caldersht, and with our ideals and our belief in happiness and goodness stronger than before. Melodrama can make us weep more; farce can make us laugh more; but when the curtain has fallen, they leave nothing behind.. They bring us nothing, because they demand nothing from us. They are excitements, not inf作者: 紡織品 時間: 2025-3-28 12:25
Andrzej Dulny,Paul Heinisch,Andreas Hotho,Anna Krauseht, and with our ideals and our belief in happiness and goodness stronger than before. Melodrama can make us weep more; farce can make us laugh more; but when the curtain has fallen, they leave nothing behind.. They bring us nothing, because they demand nothing from us. They are excitements, not inf作者: deadlock 時間: 2025-3-28 17:40
Faraz Khoshbakhtian,Gaurav Oberoi,Dionne Aleman,Siddhartha Asthana possibly by striking where many painters and poets have struck before. Certainly Helen of Troy was still a name to conjure with, and Dr. Todhunter conjured successfully. Among the many hopeful signs of a revival of higher drama his . holds an important place.. We believe he intends to renew the att作者: agitate 時間: 2025-3-28 20:48
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..978-3-031-70367-6978-3-031-70368-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: entail 時間: 2025-3-29 01:18 作者: 鎮(zhèn)壓 時間: 2025-3-29 05:58 作者: BRACE 時間: 2025-3-29 09:17 作者: 的是兄弟 時間: 2025-3-29 14:25
Continuously Deep Recurrent Neural Networksaper, we introduce a new class of recurrent neural models based on a fundamentally different type of topological organization than the conventionally used deep recurrent networks, and directly inspired by the way cortical networks in the brain process information at multiple temporal scales. We expl作者: 不再流行 時間: 2025-3-29 15:55 作者: 高貴領(lǐng)導(dǎo) 時間: 2025-3-29 22:14 作者: ALT 時間: 2025-3-30 03:47 作者: 復(fù)習(xí) 時間: 2025-3-30 04:20 作者: Militia 時間: 2025-3-30 11:07
FairFlow: An Automated Approach to?Model-Based Counterfactual Data Augmentation for NLPhese inherent biases often result in detrimental effects in various applications. Counterfactual Data Augmentation (CDA), which seeks to balance demographic attributes in training data, has been a widely adopted approach to mitigate bias in natural language processing. However, many existing CDA app作者: 友好 時間: 2025-3-30 13:37
GrINd: Grid Interpolation Network for?Scattered Observationsntific domains. Traditional methods rely on dense grid-structured data, limiting their applicability in scenarios with sparse observations. To address this challenge, we introduce GrINd (Grid Interpolation Network for Scattered Observations), a novel network architecture that leverages the high-perf作者: 有角 時間: 2025-3-30 17:26
MEGA: Multi-encoder GNN Architecture for?Stronger Task Collaboration and?Generalizationtive node representations. However, the reliance on a single pretext task often constrains generalization across various downstream tasks and datasets. Recent advancements in multi-task learning on graphs aim to tackle this limitation by integrating multiple pretext tasks, framing the problem as a m作者: paroxysm 時間: 2025-3-30 21:00
MetaQuRe: Meta-learning from?Model Quality and?Resource Consumptiona pivotal role in neural architecture search, it is less pronounced by classical AutoML approaches. In fact, they generally focus on only maximizing predictive quality and disregard the importance of finding resource-efficient solutions. To push resource awareness further, our work explicitly explor作者: 完成才會征服 時間: 2025-3-31 01:44
Propagation Structure-Semantic Transfer Learning for?Robust Fake News Detections detection methods primarily learn the semantic features from news content or integrate structural features from propagation. However, in practical scenarios, due to the semantic ambiguity of informal language and unreliable user interactive behaviors on social media, there are inherent semantic an作者: Orthodontics 時間: 2025-3-31 07:57
Exploring Contrastive Learning for?Long-Tailed Multi-label Text Classificationge arises from the inherent complexity of the task, which is shaped by two key factors: the intricate connections between labels and the widespread long-tailed distribution of the data. To overcome this issue, one potential approach involves integrating supervised contrastive learning with classical作者: 一瞥 時間: 2025-3-31 09:55
Simultaneous Linear Connectivity of?Neural Networks Modulo Permutatione symmetries contribute to the non-convexity of the networks’ loss landscapes, since linearly interpolating between two permuted versions of a trained network tends to encounter a high loss barrier. Recent work has argued that permutation symmetries are the . sources of non-convexity, meaning there 作者: 剝皮 時間: 2025-3-31 15:24
Fast Fishing: Approximating , for?Efficient and?Scalable Deep Active Image Classificationsher Information, has demonstrated impressive performance across various datasets. However, .’s high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting . in their evaluation. This paper introduces two methods t作者: 謙卑 時間: 2025-3-31 17:49 作者: 使出神 時間: 2025-4-1 00:12 作者: painkillers 時間: 2025-4-1 04:41
