5 papers · 1 filter
SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
Yitian Zhang, Liheng Ma, Antonios Valkanas +2
Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a space of real-valued measurement functions…
MODL: Multilearner Online Deep Learning
Antonios Valkanas, Boris N. Oreshkin, Mark Coates
Online deep learning tackles the challenge of learning from data streams by balancing two competing goals: fast learning and deep learning. However, existing research primarily emp…
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng, Florence Regol, Mark Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature…
CKGConv: General Graph Convolution with Continuous Kernels
Liheng Ma, Soumyasundar Pal, Yitian Zhang +3
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph do…
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Florence Regol, Joud Chataoui, Mark Coates
Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive perfo…