CERN Accelerating science

General Talks

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2019-04-18
10:52
Rediscovering the Higgs using CERN Open Data / Mieth, Alexander Hardy (speaker) (Helsinki Institute of Physics (FI))
2019 - 0:21:12. REU; UM Semester Abroad - Student Talks External links: Talk details; Event details In : UM Semester Abroad - Student Talks

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2019-04-18
10:39
Machine Learning for Likelihood Free Inference / Le Pottier, Luc Tomas (speaker) (University of Michigan (US))
2019 - 0:23:22. REU; UM Semester Abroad - Student Talks External links: Talk details; Event details In : UM Semester Abroad - Student Talks

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2019-04-18
10:30
Gauge Fields in Deep Learning / Welling, Max (speaker) (University of Amsterdam)
Joint work with Taco Cohen, Maurice Weiler and Berkay Kicanaoglu Gauge field theory is the foundation of modern physics, including general relativity and the standard model of physics. It describes how a theory of physics should transform under symmetry transformations [...]
2019 - 1:07:02. CERN Colloquium External link: Event details In : Gauge Fields in Deep Learning

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2019-04-18
10:23
Applying Generative Models to Scientific Research / Ratnikov, Fedor (speaker) (Yandex School of Data Analysis (RU))
Surrogate generative models demonstrate extraordinary progress in current years. Although most applications are dedicated to image generation and similar commercial goals, this approach is also very promising for natural sciences, especially for tasks like fast event simulation in HEP experiments. [...]
2019 - 0:30:54. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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2019-04-18
10:12
DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC / Palazzo, Serena (speaker) (The University of Edinburgh (GB))
In this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural networks that is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MadGraph5 + Pythia8, and Delphes3 fast detector simulation. [...]
2019 - 0:20:41. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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2019-04-18
10:06
Fast Simulation Using Generative Adversarial Network in LHCB / Maevskiy, Artem (speaker) (National Research University Higher School of Economics (RU))
LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. [...]
2019 - 0:23:20. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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2019-04-18
09:59
Event Generation and Statistical Sampling with Deep Generative Models / Otten, Sydney (speaker) (Radboud Universiteit Nijmegen)
We present a study for the generation of events from a physical process with generative deep learning. To simulate physical processes it is not only important to produce physical events, but also to produce the events with the right frequency of occurrence (density). [...]
2019 - 0:25:02. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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2019-04-18
09:58
LUMIN - a deep learning and data science ecosystem for high-energy physics / Strong, Giles Chatham (speaker) (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
[LUMIN][1] aims to become a deep-learning and data-analysis ecosystem for High-Energy Physics, and perhaps other scientific domains in the future. Similar to Keras and fastai it is a wrapper framework for a graph computation library (PyTorch), but includes many useful functions to handle domain-specific requirements and problems [...]
2019 - 0:21:32. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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2019-04-17
10:12
Neural networks for the abstraction of the physical symmetries in the nature / Cho, Wonsang (speaker) (Seoul National University)
Neural networks are so powerful universal approximator of complicated patterns in large-scale data, leading the explosive developments of AI in terms of deep learning. However, in many cases, usual neural networks are trained to possess poor level of abstraction, so that the model's predictability and generalizability can be quite unstable, depending on the quality and amount of the data used for training. [...]
2019 - 0:22:01. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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2019-04-17
10:02
Learning Invariant Representations using Mutual Information Regularization / Tan, Justin (speaker) (University of Melbourne)
Invariance of learned representations of neural networks against certain sensitive attributes of the input data is a desirable trait in many modern-day applications of machine learning, such as precision measurements in experimental high-energy physics and enforcing algorithmic fairness in the social and financial domain. We present a method for enforcing this invariance through regularization of the mutual information between the target variable and the classifier output. [...]
2019 - 0:22:27. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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