SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention

• URL: http://arxiv.org/abs/2106.03898v1
• Date: Mon, 7 Jun 2021 18:18:20 GMT
• Title: SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
• Authors: Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
• Abstract summary: Collisions at the Large Hadron Collider produce variable-size sets of observed particles. Physical symmetries of decay products complicate assignment of observed particles to decay products. We introduce a novel method for constructing symmetry-preserving attention networks.
• Score: 62.43586180025247
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19\% - 35\% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are avaiable at https://github.com/Alexanders101/SPANet

Related papers

• Equivariant amortized inference of poses for cryo-EM [5.141137421503899]
  cryo-EM is a vital technique for determining 3D structure of biological molecules such as proteins and viruses. The cryo-EM reconstruction problem is challenging due to the high noise levels, the missing poses of particles, and the computational demands of processing large datasets. A promising solution to these challenges lies in the use of amortized inference methods, which have shown particular efficacy in pose estimation for large datasets.
  arXiv  Detail & Related papers  (2024-06-01T11:36:29Z)
• Universal Neural Functionals [67.80283995795985]
  A challenging problem in many modern machine learning tasks is to process weight-space features. Recent works have developed promising weight-space models that are equivariant to the permutation symmetries of simple feedforward networks. This work proposes an algorithm that automatically constructs permutation equivariant models for any weight space.
  arXiv  Detail & Related papers  (2024-02-07T20:12:27Z)
• Modal analysis on quantum computers via qubitization [0.0]
  We take up some simple examples of (classical) coupled oscillators and show how the algorithm works by using qubitization methods based on a sparse structure of the matrix. We also give rough estimates of the necessary number of physical qubits and actual runtime it takes when carried out on a fault-tolerant quantum computer.
  arXiv  Detail & Related papers  (2023-07-14T17:00:02Z)
• PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics [64.5726087590283]
  We present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full 6-dimensional Lorentz symmetry. We show that the resulting network outperforms all existing competitors despite much lower model complexity.
  arXiv  Detail & Related papers  (2022-11-01T13:36:50Z)
• Understanding the Covariance Structure of Convolutional Filters [86.0964031294896]
  Recent ViT-inspired convolutional networks such as ConvMixer and ConvNeXt use large-kernel depthwise convolutions with notable structure. We first observe that such learned filters have highly-structured covariance matrices, and we find that covariances calculated from small networks may be used to effectively initialize a variety of larger networks.
  arXiv  Detail & Related papers  (2022-10-07T15:59:13Z)
• Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness [68.97830259849086]
  Most datasets only capture a simpler subproblem and likely suffer from spurious features. We study adversarial robustness - a local generalization property - to reveal hard, model-specific instances and spurious features. Unlike in other applications, where perturbation models are designed around subjective notions of imperceptibility, our perturbation models are efficient and sound. Surprisingly, with such perturbations, a sufficiently expressive neural solver does not suffer from the limitations of the accuracy-robustness trade-off common in supervised learning.
  arXiv  Detail & Related papers  (2021-10-21T07:28:11Z)
• Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations [79.71184760864507]
  This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA) In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. We show that the new method class has characteristics not found in previous alignment methods.
  arXiv  Detail & Related papers  (2020-09-28T15:05:39Z)
• Complexity continuum within Ising formulation of NP problems [0.0]
  Minimisation of the Ising Hamiltonian is known to be NP-hard problem for certain interaction matrix classes. We propose to identify computationally simple instances with an optimisation simplicity criterion' Such simplicity can be found for a wide range of models from spin glasses to k-regular maximum cut problems.
  arXiv  Detail & Related papers  (2020-08-02T11:36:38Z)
• Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers [42.93754828584075]
  We present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR) Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. It provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence.
  arXiv  Detail & Related papers  (2020-06-05T17:09:16Z)

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.