| Year | Rank | Type | Title / Venue / Authors |
|---|---|---|---|
| 2024 | J | jnl |
CoRR
|
| 2024 | J | jnl |
CoRR
|
| 2024 | J | jnl |
CoRR
|
| 2023 | — | conf |
MLSP
|
| 2023 | J | jnl |
CoRR
|
| 2023 | A* | conf |
CVPR
|
| 2023 | J | jnl |
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity.
CoRR
|
| 2022 | J | jnl |
CoRR
|
| 2022 | J | jnl |
CoRR
|
| 2019 | — | conf |
MLSP
|
| 2019 | — | conf |
MLSP
|
| 2019 | Misc | conf |
ICASSP
|
| 2017 | Misc | conf |
ICASSP
|
| 2016 | — | conf |
IEEE SENSORS
|
| 2014 | — | conf |
MLSP
|
| 2012 | Misc | conf |
ICASSP
|
| 2012 | — | conf |
MLSP
|
| 2011 | — | conf |
MLSP
|