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Found 52 papers, 20 papers with code
FinAudio: A Benchmark for Audio Large Language Models in Financial Applications
We first define three tasks based on the unique characteristics of the financial domain: 1) ASR for short financial audio, 2) ASR for long financial audio, and 3) summarization of long financial audio.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks.
Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
Quantum computing promises advantages over classical computing.
FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation
Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval.
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
To address this issue, in this paper, we introduce a novel approach using the reinforcement learning method with quantum-inspired simulated annealing policy neural network to navigate the vast discrete space of chemical structures intelligently.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Financial LLMs hold promise for advancing financial tasks and domain-specific applications.
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing
However, the resulting model still consumes a large amount of GPU memory.
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.
Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
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.
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination
In this paper, we provide an empirical examination of LLMs' hallucination behaviors in financial tasks.
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Financial sentiment analysis is critical for valuation and investment decision-making.
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity.
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements.
FinGPT: Open-Source Financial Large Language Models
While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data.
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.
Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems
In this paper, we investigate deep reinforcement learning to control traffic lights, and both theoretical analysis and numerical experiments show that the intelligent behavior ``greenwave" (i. e., a vehicle will see a progressive cascade of green lights, and not have to brake at any intersection) emerges naturally a grid road network, which is proved to be the optimal policy in an avenue with multiple cross streets.
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.
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.
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels.
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.
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading.
Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach
In particular, we quantify the prediction power by calculating the linear correlations between the feature weights of a DRL agent and the reference feature weights, and similarly for machine learning methods.
Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure
Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in resource-restricted environments is still a very challenging problem.
Convolutional Graph-Tensor Net for Graph Data Completion
Graph data completion is a fundamentally important issue as data generally has a graph structure, e. g., social networks, recommendation systems, and the Internet of Things.
Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification
In the end, our experimental results indicate that tensor network models are effective for tiny object classification problem and potentially will beat state-of-the-art.
Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation
To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph.
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization
To this end, we propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph.
Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition
Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling.
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.
Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System
We demonstrate three types of directed communications to show the effect of directions of social influence on the entire network utility and individual utility.
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
In addition, our methods are based on the multi-task learning framework and mitigate the data sparsity issue.
Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction
In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states.
Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis
Liquidation is the process of selling a large number of shares of one stock sequentially within a given time frame, taking into consideration the costs arising from market impact and a trader's risk aversion.
Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation
Portfolio allocation is crucial for investment companies.
Statistical Finance
Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks
Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities.
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions.
Deep Reinforcement Learning for Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe.
Practical Deep Reinforcement Learning Approach for Stock Trading
We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis
We propose a novel multilinear dynamical system (MLDS) in a transform domain, named $\mathcal{L}$-MLDS, to model tensor time series.
Information Scaling Law of Deep Neural Networks
In this paper, we propose a novel information scaling law scheme that can interpret the network's inner organization by information theory.
An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems
As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities.
Multidimensional Data Tensor Sensing for RF Tomographic Imaging
The recently proposed transform-based tensor model is more appropriate for sensory data processing, as it helps exploit the geometric structures of the three-dimensional target and improve the recovery precision.
Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization
Global Positioning System (GPS) becomes invalid in indoor environments due to the non-line-of-sight issue, so it is urgent to develop a real-time high-accuracy localization approach for smartphones.
Fourth-order Tensors with Multidimensional Discrete Transforms
The multidimensional feature and huge volume of big data put urgent requirements to the development of multilinear modeling tools and efficient algorithms.
Numerical Analysis Information Theory Information Theory
3D seismic data denoising using two-dimensional sparse coding scheme
Seismic data denoising is vital to geophysical applications and the transform-based function method is one of the most widely used techniques.
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination
Sparse coding (SC) is an automatic feature extraction and selection technique that is widely used in unsupervised learning.
Graph Regularized Tensor Sparse Coding for Image Representation
Sparse coding (SC) is an unsupervised learning scheme that has received an increasing amount of interests in recent years.
Low-tubal-rank Tensor Completion using Alternating Minimization
The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data.
Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization
In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy.
