8 citations · 20 across the 12 of their papers we have counts for
12 papers
TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices
Mingxue Xu, Yao Lei Xu, Danilo P. Mandic
Small Language Models (SLMs, or on-device LMs) have significantly fewer parameters than Large Language Models (LLMs). They are typically deployed on low-end devices, like mobile ph…
TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition
Mingxue Xu, Yao Lei Xu, Danilo P. Mandic
High-dimensional token embeddings underpin Large Language Models (LLMs), as they can capture subtle semantic information and significantly enhance the modelling of complex language…
Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domains
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Despite the omnipresence of tensors and tensor operations in modern deep learning, the use of tensor mathematics to formally design and describe neural networks is still under-expl…
Complexity-based Financial Stress Evaluation
Hongjian Xiao, Yao Lei Xu, Danilo P. Mandic
Financial markets typically exhibit dynamically complex properties as they undergo continuous interactions with economic and environmental factors. The Efficient Market Hypothesis…
Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation
Yuyang Miao, Yao Xu, Danilo Mandic
Predicting metro passenger flow precisely is of great importance for dynamic traffic planning. Deep learning algorithms have been widely applied due to their robust performance in…
Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Analytics of financial data is inherently a Big Data paradigm, as such data are collected over many assets, asset classes, countries, and time periods. This represents a challenge…
Reducing Computational Complexity of Tensor Contractions via Tensor-Train Networks
Ilya Kisil, Giuseppe G. Calvi, Kriton Konstantinidis +2
There is a significant expansion in both volume and range of applications along with the concomitant increase in the variety of data sources. These ever-expanding trends have highl…
MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting
Yaobin Xu, Weitang Liu, Zhongyi Jiang +4
Traffic forecasting is a core element of intelligent traffic monitoring system. Approaches based on graph neural networks have been widely used in this task to effectively capture…
Graph Theory for Metro Traffic Modelling
Bruno Scalzo Dees, Yao Lei Xu, Anthony G. Constantinides +1
A unifying graph theoretic framework for the modelling of metro transportation networks is proposed. This is achieved by first introducing a basic graph framework for the modelling…
Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting
Yao Lei Xu, Giuseppe G. Calvi, Danilo P. Mandic
Recurrent Neural Networks (RNNs) represent the de facto standard machine learning tool for sequence modelling, owing to their expressive power and memory. However, when dealing wit…
Tensor Networks for Multi-Modal Non-Euclidean Data
Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Modern data sources are typically of large scale and multi-modal natures, and acquired on irregular domains, which poses serious challenges to traditional deep learning models. The…
Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequence
Yao Lei Xu, Danilo P. Mandic
Recurrent Neural Networks (RNNs) are among the most successful machine learning models for sequence modelling, but tend to suffer from an exponential increase in the number of para…