default search action
Enmao Diao
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [j5]Enmao Diao
, Taposh Banerjee
Large Deviation Analysis of Score-Based Hypothesis Testing. IEEE Access 12: 117691-117700 (2024) - [j4]Yan Zhang
A Data-Efficient Deep Learning Method for Rough Surface Clutter Reduction in GPR Images. IEEE Trans. Geosci. Remote. Sens. 62: 1-10 (2024) - [j3]Suya Wu
Quickest Change Detection for Unnormalized Statistical Models. IEEE Trans. Inf. Theory 70(2): 1220-1232 (2024) - [c16]Yuzhe Gu, Enmao Diao:
ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers. EMNLP 2024: 10084-10096 - [i20]Enmao Diao, Qi Le, Suya Wu, Xinran Wang, Ali Anwar, Jie Ding, Vahid Tarokh:
ColA: Collaborative Adaptation with Gradient Learning. CoRR abs/2404.13844 (2024) - [i19]Yuzhe Gu, Enmao Diao:
ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers. CoRR abs/2404.19441 (2024) - [i18]Qi Le, Enmao Diao, Xinran Wang, Vahid Tarokh, Jie Ding, Ali Anwar:
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation. CoRR abs/2409.04986 (2024) - 2023
- [b1]Enmao Diao:
Efficient and Collaborative Methods for Distributed Machine Learning. Duke University, Durham, NC, USA, 2023 - [c15]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Score-based Quickest Change Detection for Unnormalized Models. AISTATS 2023: 10546-10565 - [c14]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. ICLR 2023 - [c13]Enmao Diao, Eric W. Tramel, Jie Ding, Tao Zhang:
Semi-Supervised Federated Learning for Keyword Spotting. ICME Workshops 2023: 466-469 - [c12]Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh:
Robust Quickest Change Detection for Unnormalized Models. UAI 2023: 2314-2323 - [i17]Suya Wu, Enmao Diao, Taposh Banerjee, Jie Ding, Vahid Tarokh:
Quickest Change Detection for Unnormalized Statistical Models. CoRR abs/2302.00250 (2023) - [i16]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. CoRR abs/2302.05601 (2023) - [i15]Enmao Diao, Eric W. Tramel, Jie Ding, Tao Zhang:
Semi-Supervised Federated Learning for Keyword Spotting. CoRR abs/2305.05110 (2023) - 2022
- [j2]Suya Wu
Score-Based Hypothesis Testing for Unnormalized Models. IEEE Access 10: 71936-71950 (2022) - [c11]Qi Le, Enmao Diao, Xinran Wang, Ali Anwar
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. IEEECONF 2022: 1157-1163 - [c10]Enmao Diao, Jie Ding, Vahid Tarokh:
Multimodal Controller for Generative Models. CVMI 2022: 109-121 - [c9]Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg:
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. DCC 2022: 1-10 - [c8]Enmao Diao, Jie Ding, Vahid Tarokh:
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. NeurIPS 2022 - [c7]Enmao Diao, Jie Ding, Vahid Tarokh:
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. NeurIPS 2022 - [i14]Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg:
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow. CoRR abs/2201.03617 (2022) - [i13]Wenjing Yang, Ganghua Wang, Enmao Diao, Vahid Tarokh, Jie Ding, Yuhong Yang:
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation. CoRR abs/2206.05604 (2022) - [i12]Qi Le, Enmao Diao, Xinran Wang, Ali Anwar
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders. CoRR abs/2212.08779 (2022) - 2021
- [j1]Jie Ding
On Statistical Efficiency in Learning. IEEE Trans. Inf. Theory 67(4): 2488-2506 (2021) - [c6]Enmao Diao, Jie Ding, Vahid Tarokh:
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. ICLR 2021 - [i11]Enmao Diao, Jie Ding, Vahid Tarokh:
Gradient Assisted Learning. CoRR abs/2106.01425 (2021) - [i10]Enmao Diao, Jie Ding, Vahid Tarokh:
SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients. CoRR abs/2106.01432 (2021) - [i9]Enmao Diao, Vahid Tarokh, Jie Ding:
Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders. CoRR abs/2110.13340 (2021) - [i8]Mohammadreza Momenifar, Enmao Diao, Vahid Tarokh, Andrew D. Bragg:
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models. CoRR abs/2112.03469 (2021) - 2020
- [c5]Enmao Diao
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. DCC 2020: 3-12 - [c4]Suya Wu, Enmao Diao
Deep Clustering of Compressed Variational Embeddings. DCC 2020: 399 - [c3]Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao
Speech Emotion Recognition with Dual-Sequence LSTM Architecture. ICASSP 2020: 6474-6478 - [i7]Enmao Diao, Jie Ding, Vahid Tarokh:
Multimodal Controller for Generative Models. CoRR abs/2002.02572 (2020) - [i6]Enmao Diao, Jie Ding, Vahid Tarokh:
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. CoRR abs/2010.01264 (2020) - [i5]Jie Ding, Enmao Diao, Jiawei Zhou, Vahid Tarokh:
On Statistical Efficiency in Learning. CoRR abs/2012.13307 (2020)
2010 – 2019
- 2019
- [c2]Enmao Diao
Restricted Recurrent Neural Networks. IEEE BigData 2019: 56-63 - [i4]Enmao Diao, Jie Ding, Vahid Tarokh:
Distributed Lossy Image Compression with Recurrent Networks. CoRR abs/1903.09887 (2019) - [i3]Enmao Diao, Jie Ding, Vahid Tarokh:
Restricted Recurrent Neural Networks. CoRR abs/1908.07724 (2019) - [i2]Jianyou Wang, Michael Xue, Ryan Culhane, Enmao Diao, Jie Ding, Vahid Tarokh:
Speech Emotion Recognition with Dual-Sequence LSTM Architecture. CoRR abs/1910.08874 (2019) - [i1]Suya Wu, Enmao Diao
Deep Clustering of Compressed Variational Embeddings. CoRR abs/1910.10341 (2019) - 2018
- [c1]Jie Ding, Enmao Diao
A Penalized Method for the Predictive Limit of Learning. ICASSP 2018: 4414-4418
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-15 19:30 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: