Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
(2403.07066)Abstract
Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose RS3L, a novel simulation-based SSL strategy that employs a method of re-simulation to drive data augmentation for contrastive learning. By intervening in the middle of the simulation process and re-running simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how R3SL pre-training enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.
Overview
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Introduces RS3L, a novel Self-Supervised Learning strategy leveraging re-simulation for data augmentation in contrastive learning, aimed at high-energy physics.
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RS3L enhances data augmentations through in-domain and out-of-domain re-simulations, improving model learning capabilities and handling uncertainties.
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Demonstrates RS3L's efficacy in jet tagging, outperforming fully-supervised methods by developing a backbone model using graph-based architectures.
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Shows the potential of RS3L for developing robust foundation models beyond high-energy physics, suggesting its applicability in various domains reliant on simulation.
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models (RS3L)
Introduction
Self-Supervised Learning (SSL) strategies are instrumental for pre-training machine learning models, enabling them to learn powerful representations from unlabeled data. These representations are crucial as they can be fine-tuned for various downstream tasks. In the work at hand, a novel SSL methodology named RS3L is introduced, focusing on leveraging re-simulation for data augmentation in contrastive learning frameworks. This method is particularly applied to the domain of high-energy physics (HEP), wherein it demonstrates significant potential for developing comprehensive foundation models capable of discrimination tasks and uncertainty mitigation. By intervening in a simulation process and generating multiple realizations of an event, RS3L ensures data augmentation covers the complete physics-driven variations, thus enhancing the model's learning capabilities.
The RS3L Strategy
The essence of RS3L lies in its unique approach to generating data augmentations through re-simulation, a process that further splits into in-domain and out-of-domain augmentations. The former re-samples with a different seed under the same simulator settings, while the latter explores variations by altering simulator configurations or utilizing different simulators. This strategy not only enhances the domain completeness of the augmentation set but also introduces a robust mechanism to account for uncertainties inherent in simulations versus real-world data discrepancies.
Experiments and Results
The practical application of RS3L was demonstrated by focusing on jet tagging, a crucial task in HEP for classifying jets originating from different elementary particles. Key contributions include:
- Development of the RS3L backbone model, harnessing graph-based architectures for jet data representation in an 8D latent space through contrastive learning.
- A comprehensive dataset created for the community, facilitating further research on SSL strategies.
- A systematic study highlighting RS3L's competence over fully-supervised learning methods, particularly showcased through improved model performance in discrimination tasks and enhanced robustness against simulation-induced uncertainties.
Implications and Future Directions
RS3L represents a significant stride toward developing robust and efficient foundation models for HEP. It illustrates how self-supervised pre-training, powered by physics-informed data augmentations, lays the groundwork for versatile AI models adaptable to a wide array of tasks. This approach is not confined to HEP but has potential applications across various domains where simulation plays a pivotal role in research and development. Future explorations might revolve around expanding the range of self-supervised learning strategies and the scale of pre-training datasets to further refine the performance and applicability of RS3L.
Conclusion
RS3L stands out by marrying the concepts of re-simulation and contrastive learning, creating a powerful framework for self-supervised representation learning. This methodology goes beyond conventional approaches by embedding physically recognizable uncertainties and variabilities directly into the learning process, thus promising a new horizon for foundation models in HEP and beyond. With its ability to adapt to improved simulations and the potential for application in other scientific domains, RS3L paves the way for more generalized, robust, and scalable machine learning models in science.
Data Availability
The RS3L dataset is open for access, providing a valuable resource for further exploration and development in improving SSL strategies in HEP and other fields.
Acknowledgments
The development of RS3L benefitted from collaborations across various research institutions and was supported by grants from the US Department of Energy (DOE), the National Science Foundation (NSF), and the Alexander von Humboldt foundation. Their contributions highlight the collaborative spirit and support necessary for advancing innovative AI research in the scientific community.