J
Jie Ding
Researcher at University of Minnesota
Publications - 94
Citations - 1022
Jie Ding is an academic researcher from University of Minnesota. The author has contributed to research in topics: Model selection & Autoregressive model. The author has an hindex of 11, co-authored 82 publications receiving 514 citations. Previous affiliations of Jie Ding include Duke University & Tsinghua University.
Papers
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Journal ArticleDOI
Model Selection Techniques: An Overview
TL;DR: An integrated and practically relevant discussions on theoretical properties of state-of-the-art model selection approaches are provided, in terms of their motivation, large sample performance, and applicability.
Posted Content
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Enmao Diao,Jie Ding,Vahid Tarokh +2 more
TL;DR: This work proposes a new federated learning framework named HeteroFL to address heterogeneous clients equipped with very different computation and communication capabilities, and challenges the underlying assumption of existing work that local models have to share the same architecture as the global model.
Proceedings ArticleDOI
Speech Emotion Recognition with Dual-Sequence LSTM Architecture
TL;DR: This work proposes a new dual-level model that predicts emotions based on both MFCC features and mel-spectrograms produced from raw audio signals, and is comparable with multimodal models that leverage textual information as well as audio signals.
Journal ArticleDOI
Perturbation Analysis of Orthogonal Matching Pursuit
Jie Ding,Laming Chen,Yuantao Gu +2 more
TL;DR: The performance of OMP is analyzed under general perturbations, which means both y and Φ are perturbed and it is proved that the sufficient conditions for support recovery of the best k-term approximation of x can be relaxed, and the support can even be recovered in the order of the entries' magnitude.
Journal ArticleDOI
Bridging AIC and BIC: A New Criterion for Autoregression
TL;DR: In this paper, the authors proposed a new information criterion for order selection for an autoregressive model fitted to time series data, which has the benefits of the two well-known model selection techniques: the Akaike information criterion and the Bayesian information criterion.