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Found 9 papers, 0 papers with code
Feature Manipulation for DDPM based Change Detection
Our method focuses on manipulating the feature map extracted from the Diffusion Model to be more semantically useful, and for this, we propose two methods: Feature Attention and FDAF.
Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies
In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, contingent upon the volume of simulations conducted.
Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble
In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization.
Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach
Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions.
Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations
With the rapid development of artificial intelligence technology, Transformer structural pre-training model has become an important tool for large language model (LLM) tasks.
Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research
In recent years, advancements in natural language processing (NLP) have been fueled by deep learning techniques, particularly through the utilization of powerful computing resources like GPUs and TPUs.
Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
This paper introduces a deep learning-based prediction method for autonomous driving lane change behavior, aiming to facilitate safe lane changes and thereby improve road safety.
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions.
Utilizing Deep Learning for Enhancing Network Resilience in Finance
In the age of the Internet, people's lives are increasingly dependent on today's network technology.
