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Found 8 papers, 3 papers with code
No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity.
The FinBen: An Holistic Financial Benchmark for Large Language Models
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models
This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, and Double DQN.
Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing
The resulting dynamic feature-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model.
SGDP: A Stream-Graph Neural Network Based Data Prefetcher
This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP).
Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks
Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy.
Investigation of wind pressures on tall building under interference effects using machine learning techniques
This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming.
Predicting wind pressures around circular cylinders using machine learning techniques
Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance.
