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CERN Document Server 94 records found  1 - 10nextend  jump to record: Search took 0.70 seconds. 
1.
Advanced Generative Adversarial Network Techniques / Glombitza, Jonas (speaker) (Rheinisch-Westfaelische Tech. Hoch. (DE))
2019 - 1:11:45. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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2.
Close-out / Schramm, Steven Randolph (speaker) (Universite de Geneve (CH))
2019 - 0:05:38. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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3.
Fast Deep Learning on FPGAs for the Phase-II L0 Muon Barrel Trigger of the ATLAS Experiment / Sabetta, Luigi (speaker) (Sapienza Universita e INFN, Roma I (IT))
The Level-0 Muon Trigger system of the ATLAS experiment will undergo a full upgrade for HL-LHC to stand the challenging performances requested with the increasing instantaneous luminosity. The upgraded trigger system foresees to send RPC raw hit data to the off-detector trigger processors, where the trigger algorithms run on new generation of Field-Programmable Gate Arrays (FPGAs). [...]
2019 - 0:18:21. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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4.
NeuralRinger: An Ensemble of Neural Networks Fed from Calorimeter Ring Sums for Triggering on Electrons / Spolidoro Freund, Werner (speaker) (Federal University of of Rio de Janeiro (BR))
In 2017, the ATLAS experiment implemented an ensemble of neural networks (NeuralRinger algorithm) dedicated to reduce the latency of the first, fast, online software (HLT) selection stage for electrons with transverse energy above 15 GeV. In order to minimize detector response and shower development fluctuations, and being inspired in the ensemble of likelihood models currently operating in the offline and final HLT selections, the ensemble comprises Multi-Layer Perceptron (MLP) models tuned for pseudo-rapidity and transverse energy bins. [...]
2019 - 0:18:52. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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5.
GroomRL: jet grooming through reinforcement learning / Dreyer, Frederic Alexandre (speaker) (Oxford)
We introduce a novel implementation of a reinforcement learning algorithm which is adapted to the problem of jet grooming, a crucial component of jet physics at hadron colliders. We show that the grooming policies trained using a Deep Q-Network model outperform state-of-the-art tools used at the LHC such as Recursive Soft Drop, allowing for improved resolution of the mass of boosted objects. [...]
2019 - 0:20:33. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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6.
Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production / Alonso Monsalve, Saul (speaker) (CERN)
We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter values that give images that best match the true images. [...]
2019 - 0:18:44. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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7.
Learning representations of irregular particle-detector geometry with distance-weighted graph networks / Kieseler, Jan (speaker) (CERN)
We explore the possibility of using graph networks to deal with irregular-geometry detectors when reconstructing particles. Thanks to their representation-learning capabilities, graph networks can exploit the detector granularity, while dealing with the event sparsity and the irregular detector geometry. [...]
2019 - 0:20:53. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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8.
ML Techniques for heavy flavour identification in CMS / Bols, Emil Sorensen (speaker) (Vrije Universiteit Brussel (BE))
Jet flavour identification is a fundamental component for the physics program of the LHC-based experiments. The presence of multiple flavours to be identified leads to a multiclass classification problem. [...]
2019 - 0:27:28. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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9.
Feature ranking based on subtraction methods / Glaysher, Paul (speaker) (DESY)
The input variables of ML methods in physics analysis are often highly correlated and figuring out which ones are the most important ones for the classification turns out to be a non-trivial tasks. We compare the standard method of TMVA to rank variables with a several newly developed methods based on iterative removal for the use case of a search for top pair associated Higgs production (ttH) in the Higgs to b-pair decay channel..
2019 - 0:20:43. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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10.
A hybrid deep learning approach to vertexing / Schreiner, Henry Fredrick (speaker) (University of Cincinnati (US))
In the transition to Run 3 in 2021, LHCb will undergo a major luminosity upgrade, going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event, and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. [...]
2019 - 0:19:40. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop
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