Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Ding, Zhicheng"'
The importance of recommender systems is growing rapidly due to the exponential increase in the volume of content generated daily. This surge in content presents unique challenges for designing effective recommender systems. Key among these challenge
Externí odkaz:
http://arxiv.org/abs/2408.04211
This paper aims to address the challenge of sparse and missing data in recommendation systems, a significant hurdle in the age of big data. Traditional imputation methods struggle to capture complex relationships within the data. We propose a novel a
Externí odkaz:
http://arxiv.org/abs/2407.10078
Prompt recovery, a crucial task in natural language processing, entails the reconstruction of prompts or instructions that language models use to convert input text into a specific output. Although pivotal, the design and effectiveness of prompts rep
Externí odkaz:
http://arxiv.org/abs/2407.05233
Publikováno v:
Proceedings of the 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS), 2024, pp. 77-81
This paper presents a novel approach to enhance image-to-image generation by leveraging the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We propose a framework where LLaVA analyzes input images and generates textual des
Externí odkaz:
http://arxiv.org/abs/2406.01956
A classification prediction algorithm based on Long Short-Term Memory Network (LSTM) improved AdaBoost is used to predict virtual reality (VR) user experience. The dataset is randomly divided into training and test sets in the ratio of 7:3.During the
Externí odkaz:
http://arxiv.org/abs/2405.10515
Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model
Publikováno v:
Applied and Computational Engineering. 77 (2024) 132-137
This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs)
Externí odkaz:
http://arxiv.org/abs/2404.17123
Publikováno v:
Proceedings of the 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2024, pp. 593-597
This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twist
Externí odkaz:
http://arxiv.org/abs/2404.13880
Publikováno v:
Proceedings of the 2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), 2024, pp. 214-218
In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size
Externí odkaz:
http://arxiv.org/abs/2404.13812
Publikováno v:
Proceedings of the 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI), 2024, pp. 681-685
This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms. Leveraging a balanced VQA dataset, we investigate three distinct strategies. Fi
Externí odkaz:
http://arxiv.org/abs/2404.13565
In the pursuit of environmental sustainability, the aviation industry faces the challenge of minimizing its ecological footprint. Among the key solutions is contrail avoidance, targeting the linear ice-crystal clouds produced by aircraft exhaust. The
Externí odkaz:
http://arxiv.org/abs/2404.14441