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Found 4 papers, 2 papers with code
Dynamic Sparse Network for Time Series Classification: Learning What to "see''
The receptive field (RF), which determines the region of time series to be ``seen'' and used, is critical to improve the performance for time series classification (TSC).
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs).
Multi-Objective Meta Learning
Empirically, we show the effectiveness of the proposed MOML framework in several meta learning problems, including few-shot learning, neural architecture search, domain adaptation, and multi-task learning.
Distant Transfer Learning via Deep Random Walk
Transfer learning, which is to improve the learning performance in the target domain by leveraging useful knowledge from the source domain, often requires that those two domains are very close, which limits its application scope.
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