Zobrazeno 1 - 10
of 1 423
pro vyhledávání: '"Guo, Junjie."'
Autor:
Guo, Junjie
This paper provides an empirical study explores the application of deep learning algorithms-Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-in constructing long-short stock portfolios.
Externí odkaz:
http://arxiv.org/abs/2411.13555
Publikováno v:
Nature Commun. 15, 7174 (2024)
Polarons can control carrier mobility and can also be used in the design of quantum devices. Although much effort has been directed into investigating the nature of polarons, observation of defect-related polarons is challenging due to electron-defec
Externí odkaz:
http://arxiv.org/abs/2409.06144
Infrared and visible dual-modality tasks such as semantic segmentation and object detection can achieve robust performance even in extreme scenes by fusing complementary information. Most current methods design task-specific frameworks, which are lim
Externí odkaz:
http://arxiv.org/abs/2409.00973
Infrared-visible object detection aims to achieve robust object detection by leveraging the complementary information of infrared and visible image pairs. However, the commonly existing modality misalignment problem presents two challenges: fusing mi
Externí odkaz:
http://arxiv.org/abs/2408.06123
The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memo
Externí odkaz:
http://arxiv.org/abs/2407.06529
Large language models (LLMs) exhibit robust capabilities in text generation and comprehension, mimicking human behavior and exhibiting synthetic personalities. However, some LLMs have displayed offensive personality, propagating toxic discourse. Exis
Externí odkaz:
http://arxiv.org/abs/2406.04583
This paper focuses on the application and optimization of LSTM model in financial risk prediction. The study starts with an overview of the architecture and algorithm foundation of LSTM, and then details the model training process and hyperparameter
Externí odkaz:
http://arxiv.org/abs/2405.20603
Multimodal Large Language Models (MLLMs) are widely regarded as crucial in the exploration of Artificial General Intelligence (AGI). The core of MLLMs lies in their capability to achieve cross-modal alignment. To attain this goal, current MLLMs typic
Externí odkaz:
http://arxiv.org/abs/2405.14129
Emotion Recognition in Conversation (ERC) involves detecting the underlying emotion behind each utterance within a conversation. Effectively generating representations for utterances remains a significant challenge in this task. Recent works propose
Externí odkaz:
http://arxiv.org/abs/2403.20289
Infrared-visible object detection aims to achieve robust even full-day object detection by fusing the complementary information of infrared and visible images. However, highly dynamically variable complementary characteristics and commonly existing m
Externí odkaz:
http://arxiv.org/abs/2403.00326