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Found 13 papers, 3 papers with code
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Financial LLMs hold promise for advancing financial tasks and domain-specific applications.
FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs.
CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications
The integration of Large Language Models (LLMs) into financial analysis has garnered significant attention in the NLP community.
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior
Investors and regulators can greatly benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets.
FinBen: A Holistic Financial Benchmark for Large Language Models
Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
Develop End-to-End Anomaly Detection System
To address this challenge, in this paper, we propose an end-to-end anomaly detection model development pipeline.
Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach
In this paper we are introducing a new reinforcement learning method for control problems in environments with delayed feedback.
A Hybrid Approach for Smart Alert Generation
Anomaly detection is an important task in network management.
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation
Axie infinity is a complicated card game with a huge-scale action space.
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
A fully distributed MARL algorithm is proposed to approximate the Nash equilibrium of the game.
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center
This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Reinforced Workload Distribution Fairness
Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services.
Towards Intelligent Load Balancing in Data Centers
This paper proposes Aquarius to bridge the gap between ML and networking systems and demonstrates its usage in the context of network load balancers.
