A list of useful articles collected for various works during my PhD research.
Sections are given in alphabetic order.
- Neutrino Interaction on Ar target. IOPscience (Mar 2010)
- Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation. arXiv:1802.08709
- Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I: Introduction to DUNE. arXiv:2002.02967
- Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics. arXiv:2002.03005
- Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume III: DUNE Far Detector Technical Coordination. arXiv:2002.03008
- Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume IV: Far Detector Single-phase Technology. arXiv:2002.03010
- Neutrino interaction classification with a convolutional neural network in the DUNE far detector. arXiv:2006.15052
- Digital image processing. Third edition. Book
- Image Denoising With Graph-Convolutional Neural Networks. arXiv:1905.12281
- Deep Graph-Convolutional Image Denoising. arXiv:1907.08448
- SCG-Net: Self Constructing Graph Neural Networks for Semantic Segmentation. arXiv:2009.01599
- Learning to learn by gradient descent by gradient descent. arXiv:1606.04474
- Optimization as a Model for Few-Shot Learning. ICLR 2017 (Nov 2016)
- Meta-Learning in Neural Networks: A Survey. arXiv:2004.05439
- On the Opportunities and Risks of Foundation Models. arXiv:2108.07258
- Parameter Prediction for Unseen Deep Architectures. arXiv:2110.13100
- Mean Squared Error: Love It or Leave It? IEEE Signal Processing Magazine (Jan 2009)
- Algorithms For Hyper Parameter Optimization. NIPS 2011
- Loss Functions for Neural Networks for Image Processing. arXiv:1511.08861
- Interpretability of deep learning models: A survey of results. IEEE (Aug 2017)
- The AI Methods, Capabilities and Criticality Grid. KI (Aug 2021)
- TMVA - Toolkit for Multivariate Data Analysis. arXiv:0703039
- The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector. arXiv:1708.03135
- The Single-Phase ProtoDUNE Technical Design Report. arXiv:1706.07081
- First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform. IOPscience (Dec 2020)
- Supervised quantum machine learning models are kernel methods. arXiv:2101.11020
Particle physics reviews by the Particle Data Group 2021 link.
In particular the Mathematical Tools section: probability, statistics, machine
learning, Monte Carlo techniques and Monte Carlo Event generators.
Contains also reviews about kinematics and cross sections.
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. arXiv:1612.00593
- Hierarchical Graph Clustering using Node Pair Sampling. arXiv:1806.01664
- Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN. arXiv:1811.07782
- End-to-End Hierarchical Clustering. NYU thesis (2019)
- A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications. arXiv:1905.11437
- Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning. IEEE Access
- RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. arXiv:1911.11236
- Reconstruction in an imaging calorimeter for HL-LHC. arXiv:2004.10027
- Track Seeding and Labelling with Embedded-space Graph Neural. arXiv:2007.00149
- Multi-label classification via adaptive resonance theory-based clustering. arXiv:2103.01511
- Charged particle tracking via edge-classifying interaction networks. arXiv:2103.16701
- ResSANet: Learning Geometric Information for Point Cloud Processing. Sensors (May 2021)
- Investigating Attention Mechanism in 3D Point Cloud Object Detection. arXiv:2108.00620