Search Results for author:
Found 6 papers, 2 papers with code
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring
In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation.
DIDA: Denoised Imitation Learning based on Domain Adaptation
Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications.
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control.
Avoid Overfitting User Specific Information in Federated Keyword Spotting
Federated KWS (FedKWS) could serve as a solution without directly sharing users' data.
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
We advocate the proposed methods could serve as a preliminary try to explore where to privatize for a novel non-iid scene.
Towards Enabling Meta-Learning from Target Models
Different from $\mathcal{S}$/$\mathcal{Q}$ protocol, we can also evaluate a task-specific solver by comparing it to a target model $\mathcal{T}$, which is the optimal model for this task or a model that behaves well enough on this task ($\mathcal{S}$/$\mathcal{T}$ protocol).
