91 citations · 110 across the 8 of their papers we have counts for
6 papers · 1 filter
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks
Shreyas Chaudhari, Harideep Nair, José M. F. Moura +1
Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc. These applications typically involv…
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Lavender Yao Jiang, John Shi, Mark Cheung +2
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data. A question that arises is how much performance improvement does the underlying graph s…
Evaluating and Aggregating Feature-based Model Explanations
Umang Bhatt, Adrian Weller, José M. F. Moura
A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows,…
Explainable Machine Learning in Deployment
Umang Bhatt, Alice Xiang, Shubham Sharma +7
Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactu…
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
Yang Li, José M. F. Moura
Spatial and time-dependent data is of interest in many applications. This task is difficult due to its complex spatial dependency, long-range temporal dependency, data non-stationa…
On Network Science and Mutual Information for Explaining Deep Neural Networks
Brian Davis, Umang Bhatt, Kartikeya Bhardwaj +2
In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforwar…