Search Results for author:
Found 7 papers, 4 papers with code
MCA: Moment Channel Attention Networks
In this paper, we investigate the statistical moments of feature maps within a neural network.
Energy-based Automated Model Evaluation
The core of the MDE is to establish a meta-distribution statistic, on the information (energy) associated with individual samples, then offer a smoother representation enabled by energy-based learning.
Towards Domain-Specific Features Disentanglement for Domain Generalization
Distributional shift between domains poses great challenges to modern machine learning algorithms.
Latent Processes Identification From Multi-View Time Series
Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a. k. a.
Discriminative Radial Domain Adaptation
Domain adaptation methods reduce domain shift typically by learning domain-invariant features.
iDAG: Invariant DAG Searching for Domain Generalization
In this work, we first characterize that this failure of conventional ML models in DG is attributed to an inadequate identification of causal structures.
Interventional Domain Adaptation
To address this issue, we intervene in the learning of feature discriminability using unlabeled target data to guide it to get rid of the domain-specific part and be safely transferable.
