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(yè)] 作者: Nixon 時(shí)間: 2025-3-21 16:25
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: 小歌劇 時(shí)間: 2025-3-21 23:42
Xiangyu Zheng,Guogang Tian,Sen Wang,Zhixiang Huangion stellte. Zeichnet sich in ihnen, wie die neuere Soziologie des Todes vermuten l?sst, eine ?Wiederbelebung des Todes‘ ab? Und in welcher Form gestaltet sich diese Wiederbelebung: als auf Expertise gestützter Diskurs und somit diskursive übermacht? Oder als Forum fur die vielf?ltigen Erfahrungen B作者: 元音 時(shí)間: 2025-3-22 01:52 作者: COKE 時(shí)間: 2025-3-22 04:56
Sijia Zhou,Yunwen Lei,Ata Kabán lediglich den Output optimieren, ist das zu wenig. Bedingungslose Kundenorientierung und langfristiges Denken braucht Rückgrat. Und oft genug auch die Bereitschaft, sehr lange missverstanden zu werden. Verantwortungsvolles Denken und Handeln, Wertsch?tzung für Menschen formuliert in einem ?Sinn des作者: 不近人情 時(shí)間: 2025-3-22 11:47 作者: CLAP 時(shí)間: 2025-3-22 14:28 作者: WITH 時(shí)間: 2025-3-22 17:25
Frequency Enhanced Pre-training for?Cross-City Few-shot Traffic Forecastingoping cities often face challenges in collecting sufficient training traffic data due to limited resources and outdated infrastructure. Recognizing this obstacle, the concept of cross-city few-shot forecasting has emerged as a viable approach. While previous cross-city few-shot forecasting methods i作者: 控訴 時(shí)間: 2025-3-23 00:23
Simple Graph Condensationks (GNNs) on a small condensed graph for use on the large-scale original graph. Existing methods primarily focus on aligning key metrics between the condensed and original graphs, such as gradients, output distribution and trajectories of GNNs, yielding satisfactory performance on downstream tasks. 作者: PRISE 時(shí)間: 2025-3-23 02:58 作者: 蕨類 時(shí)間: 2025-3-23 06:35 作者: paradigm 時(shí)間: 2025-3-23 10:54 作者: AMEND 時(shí)間: 2025-3-23 14:53
On the?Robustness of?Global Feature Effect Explanationsrvised learning are an essential diagnostic tool for model debugging and scientific discovery in applied sciences. However, how vulnerable they are to data and model perturbations remains an open research question. We introduce several theoretical bounds for evaluating the robustness of partial depe作者: 委屈 時(shí)間: 2025-3-23 19:58
Federated Learning with?Flexible Architecturesciencies and potential inaccuracies in model training. This limitation hinders the widespread adoption of FL in diverse and resource-constrained environments, such as those with client devices ranging from powerful servers to mobile devices. To address this need, this paper introduces Federated Lear作者: 不自然 時(shí)間: 2025-3-24 01:39
A Unified Data Augmentation Framework for?Low-Resource Multi-domain Dialogue Generationning datasets are insufficient or entirely absent. To tackle this challenge, we propose a novel data .ugmentation framework for .ulti-.omain .ialogue .eneration, referred to as .. The AMD.G framework consists of a data augmentation process and a two-stage training approach: domain-agnostic training 作者: NICHE 時(shí)間: 2025-3-24 03:33
Improving Diversity in?Black-Box Few-Shot Knowledge Distillationst KD methods require a large training set and internal access to the teacher, which are rarely available due to various restrictions. These challenges have originated a more practical setting known as ., where the student is trained with . and a . teacher. Recent approaches typically generate addit作者: Ingredient 時(shí)間: 2025-3-24 08:43 作者: esthetician 時(shí)間: 2025-3-24 14:15 作者: FIN 時(shí)間: 2025-3-24 17:01 作者: deceive 時(shí)間: 2025-3-24 20:21 作者: Contend 時(shí)間: 2025-3-25 01:15
ADR: An Adversarial Approach to?Learn Decomposed Representations for?Causal Inference the pre-treatment covariates is the common practice for the inclusion of all possible confounders, it may aggravate the issue of data imbalance. In this paper, we theoretically show that including extra information would increase the variance lower bound. Based on the causal graph, we decompose the作者: FLIT 時(shí)間: 2025-3-25 05:14
Data is Moody: Discovering Data Modification Rules from Process Event Logsding patterns in the activity sequences of an event log, while ignoring event attribute data. Event attribute data has mostly been used to predict event occurrences and process outcome, but the state of the art neglects to mine succinct and interpretable rules describing how event attribute data cha作者: Mhc-Molecule 時(shí)間: 2025-3-25 09:38 作者: Innocence 時(shí)間: 2025-3-25 12:00
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: EXCEL 時(shí)間: 2025-3-25 17:03
Improving Diversity in?Black-Box Few-Shot Knowledge Distillationning on-the-fly. Our approach helps expand and improve the diversity of the distillation set, significantly boosting student accuracy. Through extensive experiments, we achieve state-of-the-art results among other few-shot KD methods on seven image datasets. The code is available at ..作者: 四牛在彎曲 時(shí)間: 2025-3-25 21:22 作者: 種子 時(shí)間: 2025-3-26 00:56 作者: 掙扎 時(shí)間: 2025-3-26 07:22 作者: delta-waves 時(shí)間: 2025-3-26 12:27 作者: 慢跑 時(shí)間: 2025-3-26 14:52 作者: Absenteeism 時(shí)間: 2025-3-26 20:27
Machine Learning and Knowledge Discovery in Databases. Research Track978-3-031-70344-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 分貝 時(shí)間: 2025-3-26 22:45
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620545.jpg作者: 柔軟 時(shí)間: 2025-3-27 04:26
https://doi.org/10.1007/978-3-031-70344-7artificial intelligence; computer security; computer systems; computer vision; computational modelling; d作者: 喧鬧 時(shí)間: 2025-3-27 08:01
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..978-3-031-70343-0978-3-031-70344-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 上下連貫 時(shí)間: 2025-3-27 10:48
Landscape Analysis of?Stochastic Policy Gradient Methodse population and the empirical objective. In particular, our findings are agnostic to the choice of the algorithm and hold for a wide range of gradient-based methods. Consequently, we are able to recover and improve numerous existing results through the vanilla policy gradient. To the best of our kn作者: discord 時(shí)間: 2025-3-27 16:20 作者: 元音 時(shí)間: 2025-3-27 19:56
Frequency Enhanced Pre-training for?Cross-City Few-shot Traffic Forecastingime and frequency domain and trains it with self-supervised tasks encompassing reconstruction and contrastive objectives. In the fine-tuning stage, we design modules to enrich training samples and maintain a momentum-updated graph structure, thereby mitigating the risk of overfitting to the few-shot作者: 催眠藥 時(shí)間: 2025-3-28 01:08
Simple Graph Condensationoduce the Simple Graph Condensation (SimGC) framework, which aligns the condensed graph with the original graph from the input layer to the prediction layer, guided by a pre-trained Simple Graph Convolution (SGC) model on the original graph. Importantly, SimGC eliminates external parameters and excl作者: BOOM 時(shí)間: 2025-3-28 05:31 作者: 不適 時(shí)間: 2025-3-28 06:42 作者: 佛刊 時(shí)間: 2025-3-28 12:59 作者: CLEFT 時(shí)間: 2025-3-28 17:01 作者: 象形文字 時(shí)間: 2025-3-28 22:27 作者: 音的強(qiáng)弱 時(shí)間: 2025-3-29 01:53
Variable-Agnostic Causal Exploration for?Reinforcement Learningnerate intrinsic rewards or establish a hierarchy of subgoals to enhance exploration efficiency. Experimental results showcase a significant improvement in agent performance in grid-world, 2d games and robotic domains, particularly in scenarios with sparse rewards and noisy actions, such as the noto作者: IRS 時(shí)間: 2025-3-29 05:05
LayerGLAT: A?Flexible Non-autoregressive Transformer for?Single-Pass and?Multi-pass Predictionerformance in both single-pass and iterative prediction. The key idea of the proposed approach is a layer-wise training strategy that is able to emulate the generating conditions of both single-pass and multi-pass generation, leading to strong performance in both cases. The experimental results over作者: DOLT 時(shí)間: 2025-3-29 09:09 作者: 溫和女孩 時(shí)間: 2025-3-29 14:00 作者: linguistics 時(shí)間: 2025-3-29 15:40
Data is Moody: Discovering Data Modification Rules from Process Event Logs) principle, by which we choose the model with the best lossless description of the data. Additionally, we propose the greedy . algorithm to efficiently search for rules. By extensive experiments on both synthetic and real-world data, we show . indeed finds compact and interpretable rules, needs lit作者: 針葉 時(shí)間: 2025-3-29 20:51 作者: Tremor 時(shí)間: 2025-3-30 03:42 作者: 可耕種 時(shí)間: 2025-3-30 05:01
Marie C. Kempkes,Vedran Dunjko,Evert van Nieuwenburg,Jakob Spiegelberg作者: 圖表證明 時(shí)間: 2025-3-30 10:46 作者: Orgasm 時(shí)間: 2025-3-30 15:53
Hubert Baniecki,Giuseppe Casalicchio,Bernd Bischl,Przemyslaw Biecek作者: ticlopidine 時(shí)間: 2025-3-30 19:15 作者: gnarled 時(shí)間: 2025-3-31 00:28
Chenghua Gong,Xiang Li,Jianxiang Yu,Yao Cheng,Jiaqi Tan,Chengcheng Yu作者: acolyte 時(shí)間: 2025-3-31 01:03 作者: 遺留之物 時(shí)間: 2025-3-31 06:34
Alvin Heng,Abdul Fatir Ansari,Harold Sohen Büchern zurückgreifen. Die Textbeispiele (z.B. aus ?Interviews mit Sterbenden“ von Elisabeth Kühler-Ross) repr?sentieren dann nicht das jeweilige Buch, sondern einen .. Um die Typenbildung nachvollziehbar zu machen, nenne ich zun?chst das jeweilige . für den Beratungstyp. Anschlie?end stelle ich 作者: Preserve 時(shí)間: 2025-3-31 12:30
