Unpaired Translation of Point Clouds for Modeling Detector Response

• URL: http://arxiv.org/abs/2501.18674v1
• Date: Thu, 30 Jan 2025 18:53:28 GMT
• Title: Unpaired Translation of Point Clouds for Modeling Detector Response
• Authors: Mingyang Li, Michelle Kuchera, Raghuram Ramanujan, Adam Anthony, Curtis Hunt, Yassid Ayyad,
• Abstract summary: We cast this problem as an unpaired point cloud translation task. We present a novel framework for performing this mapping. We demonstrate the success of our approach in both synthetic domains and in data sourced from the Active-Target Time Projection Chamber.
• Score: 4.82984113627727
• License:
• Abstract: Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations and from experimental runs. Effective translation can assist with both noise rejection and the construction of high-fidelity simulators. Building on recent work in diffusion probabilistic models, we present a novel framework for performing this mapping. We demonstrate the success of our approach in both synthetic domains and in data sourced from the Active-Target Time Projection Chamber.

Related papers

• Generative Modeling with Bayesian Sample Inference [50.07758840675341]
  We derive a novel generative model from the simple act of Gaussian posterior inference. Treating the generated sample as an unknown variable to infer lets us formulate the sampling process in the language of Bayesian probability. Our model uses a sequence of prediction and posterior update steps to narrow down the unknown sample from a broad initial belief.
  arXiv  Detail & Related papers  (2025-02-11T14:27:10Z)
• Machine learning-enabled velocity model building with uncertainty quantification [0.41942958779358674]
  Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications. Traditional velocity model building methods are powerful but often struggle with the inherent complexities of the inverse problem. We propose a scalable methodology that integrates generative modeling, in the form of Diffusion networks, with physics-informed summary statistics.
  arXiv  Detail & Related papers  (2024-11-11T01:36:48Z)
• A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models [14.859580045688487]
  A practical bottleneck of diffusion models is their sampling speed. We propose a novel framework capable of adaptively allocating compute required for the score estimation. We show that our method could significantly improve the sampling throughput of the diffusion models without compromising image quality.
  arXiv  Detail & Related papers  (2024-08-12T05:33:45Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• RANRAC: Robust Neural Scene Representations via Random Ray Consensus [12.161889666145127]
  RANdom RAy Consensus (RANRAC) is an efficient approach to eliminate the effect of inconsistent data. We formulate a fuzzy adaption of the RANSAC paradigm, enabling its application to large scale models. Results indicate significant improvements compared to state-of-the-art robust methods for novel-view synthesis.
  arXiv  Detail & Related papers  (2023-12-15T13:33:09Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion. This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization [58.88327181933151]
  In this paper, we propose an efficient query-based membership inference attack (MIA) Experimental results indicate that the proposed method can achieve competitive performance with only two queries on both discrete-time and continuous-time diffusion models. To the best of our knowledge, this work is the first to study the robustness of diffusion models to MIA in the text-to-speech task.
  arXiv  Detail & Related papers  (2023-05-26T16:38:48Z)
• Deep Convolution for Irregularly Sampled Temporal Point Clouds [39.51507835155255]
  We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time. We propose a new deep model that is able to directly learn and predict over this irregularly sampled data, without voxelization. We present experiments on real-world weather station data and battles between large armies in StarCraft II.
  arXiv  Detail & Related papers  (2021-05-01T00:54:32Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Spatio-Temporal Functional Neural Networks [11.73856529960872]
  We propose two novel extensions of the Neural Functional Network (FNN), a temporal regression model whose effectiveness has been proven by many researchers. The proposed models are then deployed to solve a practical and challenging precipitation prediction problem in the meteorology field.
  arXiv  Detail & Related papers  (2020-09-11T21:32:35Z)

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.