6 papers
ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding
Ziyi Liang, Hamed Poursiami, Zhishun Yang +5
Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision…
Wavelet Canonical Coherence for Nonstationary Signals
Haibo Wu, Marina I. Knight, Keiland W. Cooper +2
Understanding the evolving dependence between two clusters of multivariate signals is fundamental in neuroscience and other domains where sub-networks in a system interact dynamica…
Granger Causality for Mixed Time Series Generalized Linear Models: A Case Study on Multimodal Brain Connectivity
Luiza S. C. Piancastelli, Wagner Barreto-Souza, Norbert J. Fortin +2
This paper is motivated by studies in neuroscience experiments to understand interactions between nodes in a brain network using different types of data modalities that capture dif…
Optimal Transport for Latent Integration with An Application to Heterogeneous Neuronal Activity Data
Yubai Yuan, Babak Shahbaba, Norbert Fortin +3
Detecting dynamic patterns of task-specific responses shared across heterogeneous datasets is an essential and challenging problem in many scientific applications in medical scienc…
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information
Wenzhuo Zhou, Annie Qu, Keiland W. Cooper +2
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limi…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…