13 citations · 29 across the 9 of their papers we have counts for
4 papers · 1 filter
UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization
Yiming Jiang, Jinlan Liu, Dongpo Xu +1
Adam-type algorithms have become a preferred choice for optimisation in the deep learning setting, however, despite success, their convergence is still not well understood. To this…
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Andrea Cini, Danilo Mandic, Cesare Alippi
Relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences…
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…
Fair and skill-diverse student group formation via constrained k-way graph partitioning
Alexander Jenkins, Imad Jaimoukha, Ljubisa Stankovic +1
Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration. However, defining a group of students i…