The successful integration of large language models (LLMs) into recommendation systems has proven to be a major breakthrough in recent studies, paving the way for more generic and transferable recommendations.
In our experiments, we treat discrete acoustic codes as textual data and train a masked language model using a cloze-like methodology, ultimately deriving high-quality audio representations.
To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup).
Taking the Named Entity Recognition (NER) datasets as a case study, we introduce $9$ statistical metrics for a statistical dataset evaluation framework.
The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly.
Different from previous works, AMMASurv can effectively utilize the intrinsic information within every modality and flexibly adapts to the modalities of different importance.
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
no code implementations • 9 Mar 2020 • Xianpei Han, Zhichun Wang, Jiangtao Zhang, Qinghua Wen, Wenqi Li, Buzhou Tang, Qi. Wang, Zhifan Feng, Yang Zhang, Yajuan Lu, Haitao Wang, Wenliang Chen, Hao Shao, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang, Kezun Zhang, Meng Wang, Yinlin Jiang, Guilin Qi, Lei Zou, Sen Hu, Minhao Zhang, Yinnian Lin
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.
Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.
Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, "Segment-Tree", is proposed for non-local matching cost aggregation.
We also consider the outlier version of the problem where a given number of vertices can be excluded as the outliers from the solution.
Data Structures and Algorithms Discrete Mathematics