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LUMIN aims to feel fast to use - liberal use of progress bars mean you’re able to always know when tasks will finish, and get live updates of training Use of bootstrapping to improve precision of statistics estimated from small samples Quantitative results are accompanied by uncertainties Quick, rough optimisation of random forest hyper parametersġD discriminant binning with respect to bin-fill uncertainty Optimal learning rate via cross-validated range tests Smith, 2015 This includes training, interpretation, and plottingĮxpansion of PyTorch losses to better handle weights Relevant methods and classes can take account of these weights. HEP events are normally accompanied by weight characterising the acceptance and production cross-section of that particular event, or to flatten some distribution. Unified appearance via PlotSettings class - class accepted by every plot function providing control of plot appearance, titles, colour schemes, et cetera Variety of domain-specific plotting functions Learning to Pivot with Adversarial Networks Louppe, Kagan, & Cranmer, 2016Įasy training and inference of ensembles of models:ĭefault training method fold_train_ensemble, trains a specified number of models as well as just a single modelĮnsemble class handles the (metric-weighted) construction of an ensemble, its inference, saving and loading, and interpretationĮasy exporting of models to other libraries via OnnxĮvaluation on domain-specific metrics such as Approximate Median Significance via EvalMetric classįastai-style callbacks and stateful model-fitting, allowing training, models, losses, and data to be accessible and adjustable at any pointįeature importance via auto-optimised SK-Learn random forestsĪutomatic filtering and selection of featuresįeature importance for models and ensemblesġD & 2D partial dependency plots (via PDPbox) Asimov loss Elwood & Krücker, 2018Įxotic training schemes, e.g. LorentzBoostNetworks Erdmann, Geiser, Rath, Rieger, 2018Ĭonfigurable initialisations, including LSUV Mishkin, Matas, 2016 Squeeze-excitation blocks Hu, Shen, Albanie, Sun, & Wu, 2017
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#Lumin dough series
Battaglia, Pascanu, Lai, Rezende, Kavukcuoglu, 2016, Moreno et al., 2019, and Qasim, Kieseler, Iiyama, & Pierini, 2019, with optional self-attention Vaswani et al., 2017.ġD convolutional networks for series of objects
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Residual and dense(-like) networks ( He et al. Tail - Scales down the body to the desired number of outputsĮndcap - Optional layer for use post-training to provide further computation on model outputs useful when training on a proxy objectiveĮasy loading and saving of pre-trained embedding weights Networks constructed from modular blocks:īody - Contains most of the hidden layers ModelBuilder takes parameters and modules to yield networks on-demand Stochastic Weight Averaging Izmailov et al., 2018Įntity embedding of categorical features, Guo & Berkhahn, 2016 HEP-specific data augmentation during training and inferenceįast geometric ensembles Garipov et al., 2018 Inclusion of recent deep learning techniques and practices, including: Non-network-specific methods expect Pandas DataFrame allowing their use without having to convert to FoldYielder. The FoldYielder class provides on-demand access to data stored in HDF5 format, only loading into memory what is required.Ĭonversion from ROOT and CSV to HDF5 is easy to achieve using (see examples)įoldYielder provides conversion methods to Pandas DataFrame for use with other internal methods and external packages Use with large datasets: HEP data can become quite large, making training difficult: Package Description ¶ Distinguishing Characteristics ¶ Data objects ¶
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