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5 papers · 1 filter
DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling
K. Mancini, I. Rekik
Graph Neural Networks (GNNs) have proven effective in various medical imaging applications, such as automated disease diagnosis. However, due to the local neighborhood aggregation…
HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
Chang Sun, Zhiqiang Que, Thea K. Årrestad +4
Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quanti…
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Dimitar Georgiev, Álvaro Fernández-Galiana, Simon Vilms Pedersen +4
Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner. Many applications entail th…
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…
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification
Vinicius L. S. Silva, Claire E. Heaney, Nenko Nenov +1
We propose a new method in which a generative network (GN) integrate into a reduced-order model (ROM) framework is used to solve inverse problems for partial differential equations…