Codes for building and training the neural network model described in Domain-informed neural networks for interaction localization within astroparticle experiments.

Last update: Dec 13, 2021

Related tags

Deep Learning DiNN

Overview

Domain-informed Neural Networks

Codes for building and training the neural network model described in Domain-informed neural networks for interaction localization within astroparticle experiments.

This work proposes a novel domain-informed neural network architecture for experimental physics, using particle interaction localization with the time-projection chamber (TPC) technology for dark matter research as an example application. A key feature of the signals generated within the TPC is that they enable localization of particle interactions through a process called reconstruction (i.e., inverse-problem regression). While multilayer perceptrons (MLPs) have emerged as a leading contender for reconstruction in TPCs, such a black-box approach lacks knowledge of detector physics and this can be problematic for the extreme-precision dark matter research. It is in this regard that this paper looks anew at neural network-based interaction localization and encodes prior detector knowledge, in terms of both signal characteristics and detector geometry, into the feature encoding and the output layers of a multilayer (deep) neural network. The resulting neural network, termed Domain-informed Neural Network (DiNN), limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC. This aspect of the DiNN, which has similarities with the emerging area of graph neural networks in that the neurons in the initial layers only connect to a handful of neurons in their succeeding layer, significantly reduces the number of parameters in the network in comparison to an MLP. In addition, in order to account for the detector geometry, the output layers of the network are modified using two geometric transformations to ensure the DiNN produces localizations within the interior of the detector. The end result is a neural network architecture that has 60% fewer parameters than an MLP, but that still achieves similar localization performance and provides a path to future architectural developments with improved performance because of their ability to encode additional domain knowledge into the architecture

Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

Interaction-Network-Pytorch Pytorch Implementraion of Interaction Networks for Learning about Objects, Relations and Physics. Interaction Network is a

117 Nov 5, 2022

API for RL algorithm design & testing of BCA (Building Control Agent) HVAC on EnergyPlus building energy simulator by wrapping their EMS Python API

RL - EmsPy (work In Progress...) The EmsPy Python package was made to facilitate Reinforcement Learning (RL) algorithm research for developing and tes

20 Jan 5, 2023

This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Visual-Interaction-Networks An implementation of Deepmind visual interaction networks in Pytorch. Introduction For the purpose of understanding the ch

166 Dec 6, 2022

Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

36 Dec 5, 2022

Dist2Dec: A Simplicial Neural Network for Homology Localization

6 Jun 12, 2022

Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving

GSAN Introduction Code for paper GSAN: Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving, wh

6 Oct 27, 2022

PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

3 Dec 27, 2022

CPF: Learning a Contact Potential Field to Model the Hand-object Interaction

Contact Potential Field This repo contains model, demo, and test codes of our paper: CPF: Learning a Contact Potential Field to Model the Hand-object

99 Dec 26, 2022

Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Heterogeneous INteract and aggreGatE (GraphHINGE) This is a pytorch implementation of GraphHINGE model. This is the experiment code in the following w

69 Nov 24, 2022

Releases(presubmit_zenodo_test7)

presubmit_zenodo_test7(Dec 10, 2021)

Source code(tar.gz)
Source code(zip)
presubmit_zenodo_test6(Dec 10, 2021)

Source code(tar.gz)
Source code(zip)
presubmit_zenodo_test5(Dec 10, 2021)

Remove ORCID
Source code(tar.gz)
Source code(zip)
presubmit_zenodo_test4(Dec 10, 2021)

Source code(tar.gz)
Source code(zip)
presubmit_zenodo_test3(Dec 10, 2021)

Yet another Zenodo test for community
Source code(tar.gz)
Source code(zip)
presubmit_zenodo_test2(Dec 10, 2021)

Another Zenodo test to see if Communities and ORCID set correctly.
Source code(tar.gz)
Source code(zip)
presubmit_zenodo_test(Dec 10, 2021)

This is a test release for pre-submission to see that the Zenodo hook works.
Source code(tar.gz)
Source code(zip)

Codes for building and training the neural network model described in Domain-informed neural networks for interaction localization within astroparticle experiments.

Related tags

Overview

Domain-informed Neural Networks

You might also like...

Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

API for RL algorithm design & testing of BCA (Building Control Agent) HVAC on EnergyPlus building energy simulator by wrapping their EMS Python API

This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Dist2Dec: A Simplicial Neural Network for Homology Localization

Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving

PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

CPF: Learning a Contact Potential Field to Model the Hand-object Interaction

Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Releases(presubmit_zenodo_test7)

presubmit_zenodo_test7(Dec 10, 2021)

presubmit_zenodo_test6(Dec 10, 2021)

presubmit_zenodo_test5(Dec 10, 2021)

presubmit_zenodo_test4(Dec 10, 2021)

presubmit_zenodo_test3(Dec 10, 2021)

presubmit_zenodo_test2(Dec 10, 2021)

presubmit_zenodo_test(Dec 10, 2021)

Owner

DIDACTS

Source code for The Power of Many: A Physarum Swarm Steiner Tree Algorithm

Estimation of human density in a closed space using deep learning.

Attendance Monitoring with Face Recognition using Python

PyTorch implementation of saliency map-aided GAN for Auto-demosaic+denosing

LyaNet: A Lyapunov Framework for Training Neural ODEs

A Pytorch Implementation of ClariNet

Pytorch implementation AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi

Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

Improving 3D Object Detection with Channel-wise Transformer

Self-Learned Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence

Collapse by Conditioning: Training Class-conditional GANs with Limited Data

Meaningful titles for tabs and PDF downloads! Also supports tab search.

A PyTorch implementation of Learning to learn by gradient descent by gradient descent

Python Blood Vessel Topology Analysis

Project repo for the paper SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition

Implementing SYNTHESIZER: Rethinking Self-Attention in Transformer Models using Pytorch

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL"

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

"Learning Free Gait Transition for Quadruped Robots vis Phase-Guided Controller"