Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that ma...
详细信息
Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In c...
详细信息
Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network ***,for these neural network models,it is necessary to lab.l a tremendous amount of t...
详细信息
Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network ***,for these neural network models,it is necessary to lab.l a tremendous amount of training data,which is prohibitively expensive in *** this paper,we propose OnLine Machine Learning(OLML)database which stores trained models and reuses these models in a new training task to achieve a better training effect with a small amount of training *** efficient model reuse algorithm AdaReuse is developed in the OLML ***,AdaReuse firstly estimates the reuse potential of trained models from domain relatedness and model quality,through which a group of trained models with high reuse potential for the training task could be selected ***,multi selected models will be trained iteratively to encourage diverse models,with which a better training effect could be achieved by *** evaluate AdaReuse on two types of natural language processing(NLP)tasks,and the results show AdaReuse could improve the training effect significantly compared with models training from scratch when the training data is *** on AdaReuse,we implement an OLML database prototype system which could accept a training task as an SQL-like query and automatically generate a training plan by selecting and reusing trained *** studies are conducted to illustrate the OLML database could properly store the trained models,and reuse the trained models efficiently in new training tasks.
Partial lab.l learning is a weakly supervised learning framework in which each instance is associated with multiple candidate lab.ls,among which only one is the ground-truth *** paper proposes a unified formulation th...
详细信息
Partial lab.l learning is a weakly supervised learning framework in which each instance is associated with multiple candidate lab.ls,among which only one is the ground-truth *** paper proposes a unified formulation that employs proper lab.l constraints for training models while simultaneously performing *** existing partial lab.l learning approaches that only leverage similarities in the feature space without utilizing lab.l constraints,our pseudo-lab.ling process leverages similarities and differences in the feature space using the same candidate lab.l constraints and then disambiguates noise *** experiments on artificial and real-world partial lab.l datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.
Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations bet...
详细信息
Non-line-of-sight (NLOS) imaging allows for seeing hidden scenes around corners through active sensing. Most previous algorithms for NLOS reconstruction require dense transients acquired through regular scans over a l...
In large-span grid structures with thousands of members involved, member deformation is one of the most commonly-observed damages affecting the normal service, even the safety of structures. Generally, structural main...
详细信息
Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision w...
详细信息
Situation awareness on Mars is indispensable for various downstream applications such as navigation and path planning, mapping and scientific discover. However, current Mars rover platform suffered from the absence of...
详细信息
Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used M...
详细信息
暂无评论