A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods
In this paper, an image recognition algorithm based on the combination of deep learning
and generative adversarial network (GAN) is studied, and compared with traditional image …
and generative adversarial network (GAN) is studied, and compared with traditional image …
Deception detection from linguistic and physiological data streams using bimodal convolutional neural networks
Deception detection is gaining increasing interest due to ethical and security concerns. This
paper explores the application of convolutional neural networks for the purpose of …
paper explores the application of convolutional neural networks for the purpose of …
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks
The internal structure and operation mechanism of large-scale language models are
analyzed theoretically, especially how Transformer and its derivative architectures can …
analyzed theoretically, especially how Transformer and its derivative architectures can …
Adaptive friction in deep learning: Enhancing optimizers with sigmoid and tanh function
H Zheng, B Wang, M Xiao, H Qin, Z Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
Adaptive optimizers are pivotal in guiding the weight updates of deep neural networks, yet
they often face challenges such as poor generalization and oscillation issues. To counter …
they often face challenges such as poor generalization and oscillation issues. To counter …
Harnessing earnings reports for stock predictions: A qlora-enhanced llm approach
H Ni, S Meng, X Chen, Z Zhao, A Chen… - … Conference on Data …, 2024 - ieeexplore.ieee.org
Accurate stock market predictions following earnings reports are crucial for investors.
Traditional methods, particularly classical machine learning models, struggle with these …
Traditional methods, particularly classical machine learning models, struggle with these …
Regional style and color transfer
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 …
methods often suffer from the drawback of applying style homogeneously across the entire …
Attention mechanism and context modeling system for text mining machine translation
This paper advances a novel architectural schema anchored upon the Transformer
paradigm and innovatively amalgamates the K-means categorization algorithm to augment …
paradigm and innovatively amalgamates the K-means categorization algorithm to augment …
Advanced Multimodal Deep Learning Architecture for Image-Text Matching
Image-text matching is a key multimodal task that aims to model the semantic association
between images and text as a matching relationship. With the advent of multimedia …
between images and text as a matching relationship. With the advent of multimedia …
A comparative study on enhancing prediction in social network advertisement through data augmentation
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 …
data play a critical role in the performance of predictive models. However, the development …
Evaluating modern approaches in 3d scene reconstruction: Nerf vs gaussian-based methods
Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods
in the context of 3D scene reconstruction, this study contrasts these modern approaches with …
in the context of 3D scene reconstruction, this study contrasts these modern approaches with …