Is Tokenization Needed for Masked Particle Modelling? (2409.12589v2)

Abstract: In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

• The paper demonstrates that tokenization is not essential for masked particle modelling, achieving comparable or superior performance with direct reconstruction methods.
• The research introduces methodological innovations such as enhanced decoders and alternative pre-training strategies that bypass traditional discretization techniques.
• Empirical results show strong performance gains across tasks like in-distribution and OOD classification, highlighting potential efficiency improvements in high-energy physics applications.

Analysis of "Is Tokenization Needed for Masked Particle Modelling?"

The presented paper tackles an essential challenge within high-energy physics (HEP) related to self-supervised learning (SSL) methodologies. Specifically, it evaluates whether the traditional tokenization and discretization strategies, applied widely in NLP and CV tasks, are necessary for effective masked particle modeling (MPM) in HEP contexts. The research enhances the MPM, a self-supervised learning scheme, to efficiently develop foundation models applicable to high-energy physics, without relying strictly on tokenized methodologies.

Methodological Innovations

The authors propose significant enhancements to the MPM framework by revisiting the need for tokenization and implementing more sophisticated architectural modifications. The original MPM included a Vector-Quantized Variational Autoencoder (VQVAE) to derive tokenized representations, arguing that these encapsulate rich semantic information useful for the task. However, this method has limitations concerning computational efficiency and information loss during quantization. In this paper, a shift is proposed towards more powerful decoders and alternative pre-training strategies, eliminating the complicated tokenization process.

Reconstruction Techniques

Key to their method is experimenting with multiple reconstruction tasks to improve model pre-training:

1. K-Means Tokenization: This simpler discretization technique from employing K-Means clustering rather than VQVAE displayed encouraging results due to its lower computational overhead.
2. Direct Regression: Previously viewed as less effective, the new transformed architecture draws comparable results with a sophisticated decoder setup.
3. Conditional Generative Models: Techniques such as Conditional Normalizing Flow (CNF) and Conditional Flow-Matching (CFM) are explored to form robust output distributions, showcasing a step away from traditional classification challenges tied to tokenization.
4. Set-to-Set Flow-Matching (SSFM): This novel diffusion model-based approach extends the MPM framework towards generating particle jets, demonstrating potential for high-dimensional data reconstruction.

Strong Empirical Performance

Empirical results underscore marked improvements across various metrics and diverse tasks post-adoption of enhanced strategies. Notably, transitioning from the original setup with complex tokenization to direct reconstruction methodologies leads to performance parity, if not superiority, in tasks such as in-distribution classification, OOD classification, and vertex finding.

These findings manifest that new configurations using high-capacity architecture (e.g., transformers for decoders) and alternative semi-trivial tokenization strategies maintain or enhance expressive integrity and task-specific utility of pre-trained models.

Theoretical and Practical Implications

The paper underlines tokenization’s non-essentiality for achieving superior modeling capability in physics-directed foundation models. It potentially paves the path for lighter, more efficient pre-training methodologies that eschew tokenization, saving both computational resources and development complexity. The broader implication indicates analogous strategies might apply to other domains conditioned similarly on continuous feature spaces.

Prospects in AI and HEP

As HEP seeks to more intricately integrate foundation models, the insights drawn reinforce AI's role in diversifying model outputs across tasks typically performed independently, such as classification and anomaly detection. Future explorations may experiment further with cross-disciplinary model types and assess techniques that blend transformer architectures with advanced generative capabilities, continuing the advancement in this unique confluence of physics and machine learning.

By extensively re-evaluating pre-training strategies, this paper serves as a crucial reflection upon the practices involving tokenization and subsequent self-supervised representations that shape effective learning in complex, unordered datasets in niche application domains like HEP.