201 research outputs found
Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
The increasing luminosities of future Large Hadron Collider runs and next
generation of collider experiments will require an unprecedented amount of
simulated events to be produced. Such large scale productions are extremely
demanding in terms of computing resources. Thus new approaches to event
generation and simulation of detector responses are needed. In LHCb, the
accurate simulation of Cherenkov detectors takes a sizeable fraction of CPU
time. An alternative approach is described here, when one generates high-level
reconstructed observables using a generative neural network to bypass low level
details. This network is trained to reproduce the particle species likelihood
function values based on the track kinematic parameters and detector occupancy.
The fast simulation is trained using real data samples collected by LHCb during
run 2. We demonstrate that this approach provides high-fidelity results.Comment: Proceedings for 19th International Workshop on Advanced Computing and
Analysis Techniques in Physics Research. (Fixed typos and added one missing
reference in the revised version.
Cherenkov Detectors Fast Simulation Using Neural Networks
We propose a way to simulate Cherenkov detector response using a generative
adversarial neural network to bypass low-level details. This network is trained
to reproduce high level features of the simulated detector events based on
input observables of incident particles. This allows the dramatic increase of
simulation speed. We demonstrate that this approach provides simulation
precision which is consistent with the baseline and discuss possible
implications of these results.Comment: In proceedings of 10th International Workshop on Ring Imaging
Cherenkov Detector
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learning a
distribution that faithfully recovers a reference distribution in its entirety.
However, in some cases, we may want to only learn some aspects (e.g., cluster
or manifold structure), while modifying others (e.g., style, orientation or
dimension). In this work, we propose an approach to learn generative models
across such incomparable spaces, and demonstrate how to steer the learned
distribution towards target properties. A key component of our model is the
Gromov-Wasserstein distance, a notion of discrepancy that compares
distributions relationally rather than absolutely. While this framework
subsumes current generative models in identically reproducing distributions,
its inherent flexibility allows application to tasks in manifold learning,
relational learning and cross-domain learning.Comment: International Conference on Machine Learning (ICML
Learning Generative Models with Sinkhorn Divergences
The ability to compare two degenerate probability distributions (i.e. two
probability distributions supported on two distinct low-dimensional manifolds
living in a much higher-dimensional space) is a crucial problem arising in the
estimation of generative models for high-dimensional observations such as those
arising in computer vision or natural language. It is known that optimal
transport metrics can represent a cure for this problem, since they were
specifically designed as an alternative to information divergences to handle
such problematic scenarios. Unfortunately, training generative machines using
OT raises formidable computational and statistical challenges, because of (i)
the computational burden of evaluating OT losses, (ii) the instability and lack
of smoothness of these losses, (iii) the difficulty to estimate robustly these
losses and their gradients in high dimension. This paper presents the first
tractable computational method to train large scale generative models using an
optimal transport loss, and tackles these three issues by relying on two key
ideas: (a) entropic smoothing, which turns the original OT loss into one that
can be computed using Sinkhorn fixed point iterations; (b) algorithmic
(automatic) differentiation of these iterations. These two approximations
result in a robust and differentiable approximation of the OT loss with
streamlined GPU execution. Entropic smoothing generates a family of losses
interpolating between Wasserstein (OT) and Maximum Mean Discrepancy (MMD), thus
allowing to find a sweet spot leveraging the geometry of OT and the favorable
high-dimensional sample complexity of MMD which comes with unbiased gradient
estimates. The resulting computational architecture complements nicely standard
deep network generative models by a stack of extra layers implementing the loss
function
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