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Found 4 papers, 3 papers with code
Few-Shot Class Incremental Learning via Robust Transformer Approach
This problem remains an open problem because all recent works are built upon the convolutional neural networks performing sub-optimally compared to the transformer approaches.
Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications.
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base
Therefore, an automatic approach for learning the semantics of a data source is desirable.
ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated.
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