Universal New Physics Latent Space

• URL: http://arxiv.org/abs/2407.20315v1
• Date: Mon, 29 Jul 2024 18:00:00 GMT
• Title: Universal New Physics Latent Space
• Authors: Anna Hallin, Gregor Kasieczka, Sabine Kraml, André Lessa, Louis Moureaux, Tore von Schwartz, David Shih,
• Abstract summary: We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology: different models are mapped to distinct regions in latent space, while indistinguishable models are mapped to the same region. This opens interesting new avenues on several fronts, such as model discrimination, selection of representative benchmark scenarios, and identifying gaps in the coverage of model space.

Related papers

• Geometric Trajectory Diffusion Models [58.853975433383326]
  Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
  arXiv  Detail & Related papers  (2024-10-16T20:36:41Z)
• Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks [50.29356570858905]
  We introduce the Dynamical Systems Framework (DSF), which allows a principled investigation of all these architectures in a common representation. We provide principled comparisons between softmax attention and other model classes, discussing the theoretical conditions under which softmax attention can be approximated. This shows the DSF's potential to guide the systematic development of future more efficient and scalable foundation models.
  arXiv  Detail & Related papers  (2024-05-24T17:19:57Z)
• Diffeomorphic Measure Matching with Kernels for Generative Modeling [1.2058600649065618]
  This article presents a framework for transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs) A theoretical analysis of the proposed method is presented, giving a priori error bounds in terms of the complexity of the model, the number of samples in the training set, and model misspecification.
  arXiv  Detail & Related papers  (2024-02-12T21:44:20Z)
• Improving Generative Model-based Unfolding with Schr\"{o}dinger Bridges [14.989614554242229]
  Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. We propose to use Schroedinger Bridges and diffusion models to create SBUnfold, an unfolding approach that combines the strengths of both discriminative and generative models. We show that SBUnfold achieves excellent performance compared to state of the art methods on a synthetic Z+jets dataset.
  arXiv  Detail & Related papers  (2023-08-23T18:01:01Z)
• Geometric Neural Diffusion Processes [55.891428654434634]
  We extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We show that with these conditions, the generative functional model admits the same symmetry.
  arXiv  Detail & Related papers  (2023-07-11T16:51:38Z)
• Representer Point Selection for Explaining Regularized High-dimensional Models [105.75758452952357]
  We introduce a class of sample-based explanations we term high-dimensional representers. Our workhorse is a novel representer theorem for general regularized high-dimensional models. We study the empirical performance of our proposed methods on three real-world binary classification datasets and two recommender system datasets.
  arXiv  Detail & Related papers  (2023-05-31T16:23:58Z)
• On the Transformation of Latent Space in Fine-Tuned NLP Models [21.364053591693175]
  We study the evolution of latent space in fine-tuned NLP models. We discover latent concepts in the representational space using hierarchical clustering. We compare pre-trained and fine-tuned models across three models and three downstream tasks.
  arXiv  Detail & Related papers  (2022-10-23T10:59:19Z)
• The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning [62.601681746034956]
  Self-supervised learning (SSL) has emerged as a desirable paradigm in computer vision. We propose a data-driven geometric strategy to analyze different SSL models using local neighborhoods in the feature space induced by each.
  arXiv  Detail & Related papers  (2022-09-18T18:15:38Z)
• Unveiling the Latent Space Geometry of Push-Forward Generative Models [24.025975236316846]
  Many deep generative models are defined as a push-forward of a Gaussian measure by a continuous generator, such as Generative Adversarial Networks (GANs) or Variational Auto-Encoders (VAEs) This work explores the latent space of such deep generative models. A key issue with these models is their tendency to output samples outside of the support of the target distribution when learning disconnected distributions.
  arXiv  Detail & Related papers  (2022-07-21T15:29:35Z)
• Contrastive Neighborhood Alignment [81.65103777329874]
  We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features. The target model aims to mimic the local structure of the source representation space using a contrastive loss. CNA is illustrated in three scenarios: manifold learning, where the model maintains the local topology of the original data in a dimension-reduced space; model distillation, where a small student model is trained to mimic a larger teacher; and legacy model update, where an older model is replaced by a more powerful one.
  arXiv  Detail & Related papers  (2022-01-06T04:58:31Z)
• Atlas Generative Models and Geodesic Interpolation [0.20305676256390928]
  We define the general class of Atlas Generative Models (AGMs), models with hybrid discrete-continuous latent space. We exemplify this by generalizing an algorithm for graph based geodesic to the setting of AGMs, and verify its performance experimentally.
  arXiv  Detail & Related papers  (2021-01-30T16:35:25Z)

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.