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Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach
Accurate stock market predictions following earnings reports are crucial for investors.
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 traditional Simultaneous Localization and Mapping (SLAM) systems.
3D Scene Reconstruction
Simultaneous Localization and Mapping
Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers
Cardiovascular disease (CVD) is a leading cause of death globally, necessitating precise forecasting models for monitoring vital signs like heart rate, blood pressure, and ECG.
Enhance Image-to-Image Generation with LLaVA-generated Prompts
We observe a significant improvement in the visual coherence between the generated and input images compared to traditional methods.
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.
Regional Style and Color Transfer
This paper presents a novel contribution to the field of regional style transfer.
Exploring Diverse Methods in Visual Question Answering
This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms.
Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks
This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection.
Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems
Real-time object tracking necessitates a delicate balance between speed and accuracy, a challenge exacerbated by the computational demands of deep learning methods.
Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery
Semantic segmentation for aerial imagery is a challenging and important problem in remotely sensed imagery analysis.
