Harnessing earnings reports for stock predictions: A qlora-enhanced llm approach
H Ni, S Meng, X Chen, Z Zhao, A Chen, P Li… - arXiv preprint arXiv …, 2024 - arxiv.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 …
Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training
Optimization theory serves as a pivotal scientific instrument for achieving optimal system
performance, with its origins in economic applications to identify the best investment …
performance, with its origins in economic applications to identify the best investment …
Research on prediction recommendation system based on improved markov model
Z Wu, X Wang, S Huang, H Yang… - Advances in Computer …, 2024 - clausiuspress.com
With the rapid development of the Internet and information technology, recommendation
systems are playing an increasingly important role in various applications. Traditional …
systems are playing an increasingly important role in various applications. Traditional …
Graph Unlearning: Mechanism and Future Direction for Machine Unlearning with Complex Relationships
C Qi - 2024 - preprints.org
Graph unlearning has emerged as a crucial technique in privacy-preserving applications,
particularly in scenarios where sensitive data must be erased from graph-based systems …
particularly in scenarios where sensitive data must be erased from graph-based systems …
Reducing Bias in Deep Learning Optimization: The RSGDM Approach
Currently, widely used first-order deep learning optimizers include non-adaptive learning
rate optimizers and adaptive learning rate optimizers. The former is represented by SGDM …
rate optimizers and adaptive learning rate optimizers. The former is represented by SGDM …
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models
With the rapid development of artificial intelligence technology, especially the increasingly
widespread application of question-and-answer systems, high-quality question generation …
widespread application of question-and-answer systems, high-quality question generation …
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment
This paper takes the graph neural network as the technical framework, integrates the
intrinsic connections between enterprise financial indicators, and proposes a model for …
intrinsic connections between enterprise financial indicators, and proposes a model for …
Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing
The development of Internet technology has led to a rapid increase in news information.
Filtering out valuable content from complex information has become an urgentproblem that …
Filtering out valuable content from complex information has become an urgentproblem that …
Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature
Amidst the swift evolution of social media platforms and e-commerce ecosystems, the
domain of opinion mining has surged as a pivotal area of exploration within natural …
domain of opinion mining has surged as a pivotal area of exploration within natural …
Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment
M Jiang, J Lin, H Ouyang, J Pan, S Han… - arXiv preprint arXiv …, 2024 - arxiv.org
This paper delves into the application of adversarial domain adaptation (ADA) for enhancing
credit risk assessment in financial institutions. It addresses two critical challenges: the cold …
credit risk assessment in financial institutions. It addresses two critical challenges: the cold …