208 citations · 360 across the 12 of their papers we have counts for
12 papers
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
Hui Niu, Siyuan Li, Jiahao Zheng +4
Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity. With strong capabilities in sequential dec…
Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
Saizhuo Wang, Hang Yuan, Leon Zhou +3
One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-craft…
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Hongyang Yang +6
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environmen…
Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence
Jian Guo, Saizhuo Wang, Lionel M. Ni +1
Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mains…
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Jingyang Rui +6
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement le…
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
Feng Li, Hao Zhang, Yi-Fan Zhang +5
This paper presents a comprehensive survey of vision-language (VL) intelligence from the perspective of time. This survey is inspired by the remarkable progress in both computer vi…
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
Feng Li, Hao Zhang, Shilong Liu +3
We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR…
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance
Xiao-Yang Liu, Jingyang Rui, Jiechao Gao +5
Deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators recently. However, due to the highly complex and dynamic nature of real-world ma…
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
Xiao-Yang Liu, Zechu Li, Zhuoran Yang +5
Deep reinforcement learning (DRL) has revolutionized learning and actuation in applications such as game playing and robotic control. The cost of data collection, i.e., generating…
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Zechu Li, Xiao-Yang Liu, Jiahao Zheng +3
Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning app…
VisMCA: A Visual Analytics System for Misclassification Correction and Analysis. VAST Challenge 2020, Mini-Challenge 2 Award: Honorable Mention for Detailed Analysis of Patterns of Misclassification
Huyen N. Nguyen, Jake Gonzalez, Jian Guo +2
This paper presents VisMCA, an interactive visual analytics system that supports deepening understanding in ML results, augmenting users' capabilities in correcting misclassificati…
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He +13
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…