標(biāo)題: Titlebook: Artificial Intelligence and Machine Learning; 34th Joint Benelux C Toon Calders,Celine Vens,Bart Goethals Conference proceedings 2023 The E [打印本頁] 作者: 監(jiān)督 時間: 2025-3-21 16:29
書目名稱Artificial Intelligence and Machine Learning影響因子(影響力)
書目名稱Artificial Intelligence and Machine Learning影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence and Machine Learning網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence and Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence and Machine Learning被引頻次
書目名稱Artificial Intelligence and Machine Learning被引頻次學(xué)科排名
書目名稱Artificial Intelligence and Machine Learning年度引用
書目名稱Artificial Intelligence and Machine Learning年度引用學(xué)科排名
書目名稱Artificial Intelligence and Machine Learning讀者反饋
書目名稱Artificial Intelligence and Machine Learning讀者反饋學(xué)科排名
作者: 堅毅 時間: 2025-3-22 00:02
Explaining Two Strange Learning Curves, by an increase in the variance, which we explain by a mismatch between the model and the data generating process. For the second problem, we explain the recurring increases in the learning curve by showing only two solutions are attainable by the learner. The probability of obtaining a configuratio作者: ARY 時間: 2025-3-22 01:50
,Automatic Generation of?Product Concepts from?Positive Examples, with?an?Application to?Music Strea concept, we learn a database query that is a representation of this product concept. Second, we learn product concepts and their corresponding queries when the given sets of products are associated with multiple product concepts. To achieve these goals, we propose two approaches that combine the co作者: macular-edema 時間: 2025-3-22 04:35
,A Comparative Study of?Sentence Embeddings for?Unsupervised Extractive Multi-document Summarizationels in the context of unsupervised extractive multi-document summarization. Experiments on the standard DUC’2004-2007 datasets demonstrate that the proposed methods are competitive with previous unsupervised methods and are even comparable to recent supervised deep learning-based methods. The empiri作者: MAUVE 時間: 2025-3-22 10:51
On-device Deep Learning Location Category Inference Model,ations helps limiting the GPS noise. Then, we propose a multi-modal architecture that incorporates socio-cultural information on when and for how long people typically visit venues of different categories. Finally, we compare our model with one nearest neighbor, a simple fully connected neural netwo作者: Negligible 時間: 2025-3-22 15:31 作者: 嘴唇可修剪 時間: 2025-3-22 18:48
,Examining Speaker and?Keyword Uniqueness: Partitioning Keyword Spotting Datasets for?Federated Learorks, show that the performance of the final model is stable up to at least 16 clients and models trained only on local data are clearly outperformed by federated learning. However, unique speakers for each client have a negative performance impact and it increases even more with unique keywords. Ou作者: nitroglycerin 時間: 2025-3-22 21:36 作者: 沉積物 時間: 2025-3-23 05:24 作者: novelty 時間: 2025-3-23 07:14
Boundary Stabilization of the Wave Equation, concept, we learn a database query that is a representation of this product concept. Second, we learn product concepts and their corresponding queries when the given sets of products are associated with multiple product concepts. To achieve these goals, we propose two approaches that combine the co作者: Lineage 時間: 2025-3-23 12:53 作者: 去世 時間: 2025-3-23 14:10
Bardo E. J. Bodmann,Paul J. Harrisations helps limiting the GPS noise. Then, we propose a multi-modal architecture that incorporates socio-cultural information on when and for how long people typically visit venues of different categories. Finally, we compare our model with one nearest neighbor, a simple fully connected neural netwo作者: 賞心悅目 時間: 2025-3-23 19:13
Examinatorium Privatversicherungsrechtmetry breaking tool for SAT, and in doing so also transfer modern symmetry breaking techniques to pseudo-Boolean optimization. We experimentally validate our approach on the latest pseudo-Boolean competition, as well as on hard combinatorial instances and conclude that the effect of breaking (weak) 作者: Latency 時間: 2025-3-23 23:46 作者: Psa617 時間: 2025-3-24 03:18 作者: 座右銘 時間: 2025-3-24 06:36 作者: 恃強(qiáng)凌弱的人 時間: 2025-3-24 10:54 作者: Notorious 時間: 2025-3-24 15:40
,Automatic Generation of?Product Concepts from?Positive Examples, with?an?Application to?Music Streaproving customer satisfaction. A core way they achieve this is by providing customers with an easy access to their products by structuring them in catalogues using navigation bars and providing recommendations. We refer to these catalogues as ., e.g. product categories on e-commerce websites, public作者: Admire 時間: 2025-3-24 20:06
,A View on?Model Misspecification in?Uncertainty Quantification,several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecif作者: Debark 時間: 2025-3-25 01:40 作者: right-atrium 時間: 2025-3-25 06:20 作者: 浸軟 時間: 2025-3-25 08:11 作者: 碎石 時間: 2025-3-25 15:25 作者: 改正 時間: 2025-3-25 16:49
,Symmetry and?Dominance Breaking for?Pseudo-Boolean Optimization,s problem is to introduce so-called symmetry breaking constraints, which eliminate some symmetric parts of the search space. In this paper, we focus on ., which are specified by a set of 0–1 integer linear inequalities (also known as .) and a linear objective. Symmetry breaking has already been stud作者: 微不足道 時間: 2025-3-25 23:08 作者: seduce 時間: 2025-3-26 01:17
https://doi.org/10.1007/978-3-031-52846-0study. If automatic misinformation detection is applied in a real-world setting, it is necessary to validate the methods being used. Large language models (LLMs) have produced the best results among text-based methods. However, fine-tuning such a model requires a significant amount of training data,作者: 相符 時間: 2025-3-26 08:10
https://doi.org/10.1007/978-981-97-2393-5arners, not all learning curve behavior is well understood. For instance, it is sometimes assumed that the more training data provided, the better the learner performs. However, counter-examples exist for both classical machine learning algorithms and deep neural networks, where errors do not monoto作者: Lumbar-Stenosis 時間: 2025-3-26 11:21 作者: 冷淡一切 時間: 2025-3-26 12:43
Boundary Stabilization of the Wave Equation,proving customer satisfaction. A core way they achieve this is by providing customers with an easy access to their products by structuring them in catalogues using navigation bars and providing recommendations. We refer to these catalogues as ., e.g. product categories on e-commerce websites, public作者: Crepitus 時間: 2025-3-26 19:50 作者: Medicare 時間: 2025-3-26 23:35 作者: 不規(guī)則的跳動 時間: 2025-3-27 02:42 作者: 托人看管 時間: 2025-3-27 05:40
https://doi.org/10.1007/978-3-658-46377-9 the context dependency of specificity in combination with a new special argumentation semantics. Unfortunately, their solution is restricted to argumentation systems without undercutting arguments. This paper presents a more general solution which allows for undercutting arguments and allows for an作者: 饑荒 時間: 2025-3-27 12:32 作者: tolerance 時間: 2025-3-27 14:30
Examinatorium Privatversicherungsrechts problem is to introduce so-called symmetry breaking constraints, which eliminate some symmetric parts of the search space. In this paper, we focus on ., which are specified by a set of 0–1 integer linear inequalities (also known as .) and a linear objective. Symmetry breaking has already been stud作者: 鋼筆尖 時間: 2025-3-27 18:07
Examinatorium Privatversicherungsrechtentially sensitive data. However, real world client-local data is usually biased: A single client might have access to only a few lighting conditions in computer visions, patient groups in a hospital or speakers and keywords in a smart device performing keyword spotting. We help researchers to bette作者: Ringworm 時間: 2025-3-28 01:39 作者: Spina-Bifida 時間: 2025-3-28 04:52 作者: Bumble 時間: 2025-3-28 10:14 作者: 手勢 時間: 2025-3-28 11:29
978-3-031-39143-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 儀式 時間: 2025-3-28 14:50
,Recipe for?Fast Large-Scale SVM Training: Polishing, Parallelism, and?More RAM!,both approaches to design an extremely fast dual SVM solver. We fully exploit the capabilities of modern compute servers: many-core architectures, multiple high-end GPUs, and large random access memory. On such a machine, we train a large-margin classifier on the ImageNet data set in 24?min.作者: AXIS 時間: 2025-3-28 22:49 作者: 友好關(guān)系 時間: 2025-3-28 23:39 作者: 反饋 時間: 2025-3-29 04:43 作者: Temporal-Lobe 時間: 2025-3-29 07:38
https://doi.org/10.1007/978-981-97-2393-5both approaches to design an extremely fast dual SVM solver. We fully exploit the capabilities of modern compute servers: many-core architectures, multiple high-end GPUs, and large random access memory. On such a machine, we train a large-margin classifier on the ImageNet data set in 24?min.作者: 傀儡 時間: 2025-3-29 14:02
https://doi.org/10.1007/978-3-658-46377-9literature, from straightforward state aggregation to deep learned representations, and sketch challenges that arise when combining model-based reinforcement learning with abstraction. We further show how various methods deal with these challenges and point to open questions and opportunities for further research.作者: Arrhythmia 時間: 2025-3-29 15:37
1865-0929 Mechelen, Belgium, in November 2022..The 11 papers presented in this volume were carefully reviewed and selected from 134 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning作者: Dungeon 時間: 2025-3-29 23:19 作者: OVERT 時間: 2025-3-30 02:19 作者: 抱負(fù) 時間: 2025-3-30 06:52
,A View on?Model Misspecification in?Uncertainty Quantification,ys exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecification is reliable or not. In this paper, we argue that model misspecification should receive more attention, by providing thought experiments and contextualizing these with relevant literature.
