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20172022
most citedCompressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition

21 citations · 116 across the 16 of their papers we have counts for

collaborators

21 papers

q-fin.TR20228 cited

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…

cs.CV202110 cited

Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification

Fanjie Kong, Xiao-yang Liu, Ricardo Henao

Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region…

cs.CV20202 cited

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

Daizong Liu, Shuangjie Xu, Xiao-Yang Liu +3

This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these…

cs.CV202011 cited

Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization

Daizong Liu, Xiaoye Qu, Xiao-Yang Liu +3

Query-based moment localization is a new task that localizes the best matched segment in an untrimmed video according to a given sentence query. In this localization task, one shou…

cs.LG202021 cited

Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition

Miao Yin, Siyu Liao, Xiao-Yang Liu +2

Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model si…

q-fin.ST201919 cited

DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News

Xinyi Li, Yinchuan Li, Hongyang Yang +2

Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent yea…