Search Results for author:
Found 9 papers, 5 papers with code
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science.
From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue
In this study, we present a novel hybrid algorithm, combining Levy Flight (LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information.
Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images
This paper presents an innovative method to bolster ViT performance in source-free target adaptation, beginning with an evaluation of how key, query, and value elements affect ViT outcomes.
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets.
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage.
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market.
Graph Neural Networks in IoT: A Survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on.
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance
In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning.
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance
In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve.
