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Can we infuse #structure into a time series (#TS) model from a diverse dataset so as to greatly improve #generalization on new TS coming from different datasets?
Yes, via a new principle called #Representational Time-Frequency Consistency (TF-C)
arxiv.org/abs/2206.08496 1/3
Yes, via a new principle called #Representational Time-Frequency Consistency (TF-C)
arxiv.org/abs/2206.08496 1/3
@xiangzhang1015 @MITLL @Harvard @HarvardDBMI Results suggest models can be trained on a source TS dataset and deployed on a range of target TS datasets without retraining, resulting in performance that is better than that of state-of-the-art & state-of-practice models 2/3
@xiangzhang1015 @MITLL @Harvard @HarvardDBMI Paper: arxiv.org/abs/2206.08496
Data: zitniklab.hms.harvard.edu/projects/TF-C
Code: github.com/mims-harvard/T…
Enjoyed working on this project, which is led by postdoc @xiangzhang1015 @HarvardDBMI with invaluable help from @Harvard undergraduates and @MITLL collaborators 3/3
Data: zitniklab.hms.harvard.edu/projects/TF-C
Code: github.com/mims-harvard/T…
Enjoyed working on this project, which is led by postdoc @xiangzhang1015 @HarvardDBMI with invaluable help from @Harvard undergraduates and @MITLL collaborators 3/3
