CERN Accelerating science

General Talks

Subscribe to the General Talks video podcast [?]
Últimas adquisiciones:
2017-03-24
12:41
HEP.TrkX [Vidyo] / Anderson, Dustin James (speaker) (California Institute of Technology (US))
Reconstruction of charged particle tracks is a central task in the processing of physics data at the LHC and other colliders. Current state-of-the-art tracking algorithms are based on the Kalman filter and have seen great success both offline and at trigger level. [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-24
12:38
Tutorial 3: Keras and TMVA interfaces / Wunsch, Stefan (speaker) (KIT - Karlsruhe Institute of Technology (DE))
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-24
12:38
Deep Convolutional Networks for Event Reconstruction and Particle Tagging on NOvA and DUNE / Psihas, Fernanda (speaker) (Indiana University)
Deep Convolutional Neural Networks (CNNs) have been widely applied in computer vision to solve complex problems in image recognition and analysis. In recent years many efforts have emerged to extend the use of this technology to HEP applications, including the Convolutional Visual Network (CVN), our implementation for identification of neutrino events. [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-24
12:33
Object identification with deep learning using Intel DAAL on Knights Landing processor [Vidyo] / Ojika, David Nonso (speaker) (University of Florida (US))
The problem of object recognition is computationally expensive, especially when large amounts of data is involved. Recently, techniques in deep neural networks (DNN) - including convolutional neural networks and residual neural networks - have shown great recognition accuracy compared to traditional methods (artificial neural networks, decision tress, etc.) [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-24
12:33
Using Boosted Decision Trees to look for displaced Jets in the ATLAS Calorimeter / Watts, Gordon (speaker) (University of Washington (US))
A boosted decision tree is used to identify unique jets in a recently released conference note describing a search for long lived particles decaying to hadrons in the ATLAS Calorimeter. Neutral Long lived particles decaying to hadrons are “typical” signatures in a lot of models including Hidden Valley models, Higgs Portal Models, Baryogenesis, Stealth SUSY, etc. [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-24
12:33
Application of Generative Adversarial Networks (GANs) to jet images / Paganini, Michela (speaker) (Yale University (US))
https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-24
12:33
Decorrelated Jet Substructure Tagging using Adversarial Neural Networks / Shimmin, Chase Owen (speaker) (Yale University (US))
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved discovery significance relative to existing taggers. [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-24
12:33
Top tagging with deep neural networks [Vidyo] / Pearkes, Jannicke (speaker) (University of British Columbia (CA))
Recent literature on deep neural networks for top tagging has focussed on image based techniques or multivariate approaches using high level jet substructure variables. Here, we take a sequential approach to this task by using anordered sequence of energy deposits as training inputs. [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-23
15:28
Deep-learning Top Taggers or The End of QCD? / Kasieczka, Gregor (speaker) (Eidgenoessische Technische Hochschule Zuerich (CH))
https://arxiv.org/abs/1701.08784 Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. [...]
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
2017-03-23
14:00
Session introduction
2017 - Streaming video. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

Registro completo - Registros similares
Busque también:
ScienceCinema