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

Public Models considered for emotion estimation from EEG

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

High dimensional black-box optimizer using Latent Action Monte Carlo Tree Search algorithm

PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Tool which allow you to detect and translate text.

Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Show-attend-and-tell - TensorFlow Implementation of "Show, Attend and Tell"

Learning Features with Parameter-Free Layers (ICLR 2022)

Feature extraction made simple with torchextractor

Semantic Segmentation of images using PixelLib with help of Pascalvoc dataset trained with Deeplabv3+ framework.

[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters.

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

An original implementation of "Noisy Channel Language Model Prompting for Few-Shot Text Classification"

GBIM(Gesture-Based Interaction map)

3D-aware GANs based on NeRF (arXiv).