Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture

• URL: http://arxiv.org/abs/2412.05333v1
• Date: Thu, 05 Dec 2024 19:00:14 GMT
• Title: Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
• Authors: Subash Katel, Haoyang Li, Zihan Zhao, Raghav Kansal, Farouk Mokhtar, Javier Duarte,
• Abstract summary: Self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models. This study introduces an approach to learning jet representations without hand-crafted augmentations. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations.
• Score: 10.640060102348084
• License:
• Abstract: In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions. This study introduces an approach to learning jet representations without hand-crafted augmentations using a jet-based joint embedding predictive architecture (J-JEPA), which aims to predict various physical targets from an informative context. As our method does not require hand-crafted augmentation like other common SSL techniques, J-JEPA avoids introducing biases that could harm downstream tasks. Since different tasks generally require invariance under different augmentations, this training without hand-crafted augmentation enables versatile applications, offering a pathway toward a cross-task foundation model. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations.

Related papers

• HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture [0.0]
  We present a transformer architecture-based foundation model for tasks at high-energy particle colliders. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. Our model fares well with other datasets for standard classification benchmark tasks.
  arXiv  Detail & Related papers  (2025-02-06T10:16:27Z)
• Do We Need to Design Specific Diffusion Models for Different Tasks? Try ONE-PIC [77.8851460746251]
  We propose a simple, efficient, and general approach to fine-tune diffusion models. ONE-PIC enhances the inherited generative ability in the pretrained diffusion models without introducing additional modules. Our method is simple and efficient which streamlines the adaptation process and achieves excellent performance with lower costs.
  arXiv  Detail & Related papers  (2024-12-07T11:19:32Z)
• T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data [0.0]
  Self-supervised learning (SSL) generally involves generating different views of the same sample and thus requires data augmentations. In the present work, we propose a novel augmentation-free SSL method for structured data. Our approach, T-JEPA, relies on a Joint Embedding Predictive Architecture (JEPA) and is akin to mask reconstruction in the latent space.
  arXiv  Detail & Related papers  (2024-10-07T13:15:07Z)
• Semi-Supervised One-Shot Imitation Learning [83.94646047695412]
  One-shot Imitation Learning aims to imbue AI agents with the ability to learn a new task from a single demonstration. We introduce the semi-supervised OSIL problem setting, where the learning agent is presented with a large dataset of trajectories. We develop an algorithm specifically applicable to this semi-supervised OSIL setting.
  arXiv  Detail & Related papers  (2024-08-09T18:11:26Z)
• Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey [3.1070277982608605]
  This review paper provides a thorough illustration of applications using different machine learning (ML) and deep learning (DL) approaches. The presented techniques can be applied to future hadron-hadron colliders (HHC), such as the high-luminosity LHC (HL-LHC) and the future circular collider - hadron-hadron (FCChh)
  arXiv  Detail & Related papers  (2024-03-18T16:33:29Z)
• OmniJet-$α$: The first cross-task foundation model for particle physics [0.0]
  Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a variety of downstream applications. We report significant progress on this challenge on several fronts. We demonstrate transfer learning between an unsupervised problem (jet generation) and a classic supervised task (jet tagging) with our new OmniJet-$alpha$ model.
  arXiv  Detail & Related papers  (2024-03-08T19:00:01Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information [0.0]
  We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents.
  arXiv  Detail & Related papers  (2023-11-30T19:00:02Z)
• Towards Efficient Task-Driven Model Reprogramming with Foundation Models [52.411508216448716]
  Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. This brings a critical challenge for the real-world application of foundation models: one has to transfer the knowledge of a foundation model to the downstream task.
  arXiv  Detail & Related papers  (2023-04-05T07:28:33Z)
• Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent [79.58680275615752]
  We propose an energy-efficient federated meta-learning framework. We assume each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model.
  arXiv  Detail & Related papers  (2021-05-31T08:15:44Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.