17 citations · 31 across the 5 of their papers we have counts for
5 papers
UniTS: A Unified Multi-Task Time Series Model
Shanghua Gao, Teddy Koker, Owen Queen +3
Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, w…
Graph AI in Medicine
Ruth Johnson, Michelle M. Li, Ayush Noori +2
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks (GNNs), stands out for its capability to capture intricate relationshi…
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Owen Queen, Thomas Hartvigsen, Teddy Koker +3
Interpreting time series models is uniquely challenging because it requires identifying both the location of time series signals that drive model predictions and their matching to…
Domain Adaptation for Time Series Under Feature and Label Shifts
Huan He, Owen Queen, Teddy Koker +3
Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents…
Evaluating Explainability for Graph Neural Networks
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju +1
As post hoc explanations are increasingly used to understand the behavior of graph neural networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explana…