Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

发展

https://doi.org/10.1007/978-3-322-82040-2r-class and high intra-class differences properties of fine-grained datasets, the prototype-based approach, which originally performed well in general FS classification, could not achieve the expected results. In this paper, we propose a transductive method consisting of a feature mapping module and

召集

斗志

Grundlagen der Schwingfestigkeit,. To address this problem, this work presents a new multiple object tracking approach, named VacoTrack. This method combines variable GIoU-Embedding matrix (VGE) and Kalman Filter compensation, which introduces motion compensation operation over trajectory parameters to construct virtual uniform lin

牛的细微差别

BLUSH

Linear-elastisches Werkstoffverhalten, in many Mongolian NLP applications. Recently, end-to-end neural approaches have achieved excellent results in the MMA task. However, these approaches handle morphological segmentation and morphological tagging independently, and ignore the relationship between the two subtasks. In this paper, we pr

蜈蚣

Linear-elastisches Werkstoffverhalten,tion performance for low-resource language by using multiple languages corpus, which involves initially training a neural network on the multi-language dataset, followed by fine-tuning the trained model on low-resource language. In this paper, a multi-task serial pre-training method is proposed to a

homocysteine

无辜

https://doi.org/10.1007/978-3-540-73485-7wever, suffer from inaccurate estimation of either instance-level correlation or cluster-level discrepancy of data and strongly relay on the quality of the initial text representation. In this paper, we propose a Non-outlier Pseudo-labeling-based Short Text Clustering (NPLC) method, which consists o

MELD

Preamble

,Werkstoffkennwerte bei zügiger Belastung,een largely ignored in most previous studies. In this paper, we propose a novel pairing-scoring approach to better solve the overlapping problem. In particular, we firstly extract all event triggers and arguments simultaneously. Then we cast the acquisition of the complete event as an assembly task.