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Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning.
Faster Fuzzing: Reinitialization with Deep Neural Models
We improve the performance of the American Fuzzy Lop (AFL) fuzz testing framework by using Generative Adversarial Network (GAN) models to reinitialize the system with novel seed files.
Dynamic Input Structure and Network Assembly for Few-Shot Learning
The ability to learn from a small number of examples has been a difficult problem in machine learning since its inception.
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