Improving the performance of weak supervision searches using transfer and meta-learning
This work addresses a practical limitation in weak supervision searches for experimental data analysis, though it appears incremental as it applies existing techniques to a specific domain.
The paper tackled the problem of weak supervision searches requiring large amounts of signal data for neural network training by using transfer and meta-learning to reduce signal needs, finding that these methods substantially improve performance.
Weak supervision searches have in principle the advantages of both being able to train on experimental data and being able to learn distinctive signal properties. However, the practical applicability of such searches is limited by the fact that successfully training a neural network via weak supervision can require a large amount of signal. In this work, we seek to create neural networks that can learn from less experimental signal by using transfer and meta-learning. The general idea is to first train a neural network on simulations, thereby learning concepts that can be reused or becoming a more efficient learner. The neural network would then be trained on experimental data and should require less signal because of its previous training. We find that transfer and meta-learning can substantially improve the performance of weak supervision searches.