Titlebook: Artificial Intelligence and Machine Learning; 34th Joint Benelux C Toon Calders,Celine Vens,Bart Goethals Conference proceedings 2023 The E

監(jiān)督

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)學科排名




書目名稱Artificial Intelligence and Machine Learning網(wǎng)絡公開度




書目名稱Artificial Intelligence and Machine Learning網(wǎng)絡公開度學科排名




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讀者反饋學科排名

堅毅

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

ARY

,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

macular-edema

,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

MAUVE

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

Negligible

嘴唇可修剪

,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

nitroglycerin

沉積物

novelty

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