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Implicit Neural Video Compression
[article]
2021
arXiv
pre-print
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Brehmer Markus Nagel Taco S. ...
In Proceedings of the European Conference on Com-
puter Vision (ECCV), pages 416–431, 2018. 2
[70] Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and
Changil Kim. ...
arXiv:2112.11312v1
fatcat:ogd256n4qzfc7epkankzy4ktuy
Weakly supervised causal representation learning
[article]
2022
arXiv
pre-print
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In particular, relaxing the exact manifold in the weakly supervised data space to a "fuzzy" one renders our argument for the identifiability of noise encodings and intervention targets invalid; see Brehmer ...
arXiv:2203.16437v1
fatcat:3dcaetbjkrbzzgv5n2e5waulye
Simulation-based inference methods for particle physics
[article]
2020
arXiv
pre-print
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Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate high-fidelity simulated data, but they are not well-suited for inference on the theory parameters with observed data. We explain why the likelihood function of high-dimensional LHC data cannot be explicitly evaluated, why this matters for data analysis, and reframe what the field has traditionally done to circumvent this problem. We then review new simulation-based inference
arXiv:2010.06439v2
fatcat:vc2wfdaq5razvjcz42xt7pim5m
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... thods that let us directly analyze high-dimensional data by combining machine learning techniques and information from the simulator. Initial studies indicate that these techniques have the potential to substantially improve the precision of LHC measurements. Finally, we discuss probabilistic programming, an emerging paradigm that lets us extend inference to the latent process of the simulator.
Likelihood-free inference with an improved cross-entropy estimator
[article]
2018
arXiv
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We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. ...
arXiv:1808.00973v1
fatcat:gsprxh677fhe3e77p7ddadeswe
Back to the Formula – LHC Edition
[article]
2021
arXiv
pre-print
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While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We show how symbolic regression trained on matrix-element information provides, for instance, optimal LHC observables in an easily interpretable form. We introduce the method using the effect of a dimension-6 coefficient on associated ZH production. We then validate it for the known case of CP-violation in weak-boson-fusion Higgs production, including detector effects.
arXiv:2109.10414v2
fatcat:zawknh4dxfg6xnipl2ncnmwsfi
Constraining effective field theories with machine learning
2019
Zenodo
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An important part of the LHC legacy will be precise limits on indirect effects of new physics, framed for instance in terms of an effective field theory. These measurements often involve many theory parameters and observables, which makes them challenging for traditional analysis methods. We discuss the underlying problem of "likelihood-free" inference and present powerful new analysis techniques that combine physics insights, statistical methods, and the power of machine learning. We have
doi:10.5281/zenodo.3599580
fatcat:ce7ruvsi4ncqriptdzgdsoqb7i
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... oped MadMiner, a new Python package that makes it straightforward to apply these techniques. In example LHC problems we show that the new approach lets us put stronger constraints on theory parameters than established methods, demonstrating its potential to improve the new physics reach of the LHC legacy measurements. While we present techniques optimized for particle physics, the likelihood-free inference formulation is much more general, and these ideas are part of a broader movement that is changing scientific inference in fields as diverse as cosmology, genetics, and epidemiology.
Effective LHC measurements with matrix elements and machine learning
[article]
2019
arXiv
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One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods
arXiv:1906.01578v1
fatcat:sl4s36pt7fdttmb7muzgdvtcne
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... t use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.
Monte-Carlo event generation for a two-Higgs-doublet model with maximal CP symmetry
[article]
2012
arXiv
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Rainer Stamen, Merle Reinhart, Antje Brehmer, Jennifer Kieselmann, Eric Wisotzky and Dr. Martin Spiegel. ...
arXiv:1206.7044v1
fatcat:kqds4okqefe3tmdmwffrvzscfi
Hierarchical clustering in particle physics through reinforcement learning
[article]
2020
arXiv
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2020 pre-print arXiv:2011.08191v1 fatcat:nzrtqxb3ybcmrhf3j3v6sq3bpi
Particle physics experiments often require the reconstruction of decay patterns through a hierarchical clustering of the observed final-state particles. We show that this task can be phrased as a Markov Decision Process and adapt reinforcement learning algorithms to solve it. In particular, we show that Monte-Carlo Tree Search guided by a neural policy can construct high-quality hierarchical clusterings and outperform established greedy and beam search baselines.
arXiv:2011.08191v2
fatcat:3m6tx3ny4ndvlgrcg3wj4qyp7q
The frontier of simulation-based inference
[article]
2020
arXiv
pre-print
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Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving new momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound change these developments may have on science.
arXiv:1911.01429v3
fatcat:kv32pqap5ne2hkvnekcck4hxkq
MadMiner: Machine learning-based inference for particle physics
[article]
2020
arXiv
pre-print
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Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed. This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this
arXiv:1907.10621v2
fatcat:hlgs24amf5bvnb4lupvh3bmfsq
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... per we introduce MadMiner, a Python module that streamlines the steps involved in this procedure. Wrapping around MadGraph5_aMC and Pythia 8, it supports almost any physics process and model. To aid phenomenological studies, the tool also wraps around Delphes 3, though it is extendable to a full Geant4-based detector simulation. We demonstrate the use of MadMiner in an example analysis of dimension-six operators in ttH production, finding that the new techniques substantially increase the sensitivity to new physics.
Nonconcave Utility Maximisation in the MIMO Broadcast Channel
2008
EURASIP Journal on Advances in Signal Processing
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The problem of determining an optimal parameter setup at the physical layer in a multiuser, multiantenna downlink is considered. An aggregate utility, which is assumed to depend on the users' rates, is used as performance metric. It is not assumed that the utility function is concave, allowing for more realistic utility models of applications with limited scalability. Due to the structure of the underlying capacity region, a two step approach is necessary. First, an optimal rate vector is
doi:10.1155/2009/645041
fatcat:huaw6ckrobdhfmdx3fllxc2shy
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... ined. Second, the optimal parameter setup is derived from the optimal rate vector. Two methods for computing an optimal rate vector are proposed. First, based on the differential manifold structure offered by the boundary of the MIMO BC capacity region, a gradient projection method on the boundary is developed. Being a local algorithm, the method converges to a rate vector which is not guaranteed to be a globally optimal solution. Second, the monotonic structure of the rate space problem is exploited to compute a globally optimal rate vector with an outer approximation algorithm. While the second method yields the global optimum, the first method is shown to provide an attractive tradeoff between utility performance and computational complexity.
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
[article]
2021
arXiv
pre-print
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We introduce a video compression algorithm based on instance-adaptive learning. On each video sequence to be transmitted, we finetune a pretrained compression model. The optimal parameters are transmitted to the receiver along with the latent code. By entropy-coding the parameter updates under a suitable mixture model prior, we ensure that the network parameters can be encoded efficiently. This instance-adaptive compression algorithm is agnostic about the choice of base model and has the
arXiv:2111.10302v1
fatcat:pfwpvuryfnfmzak5mkmkbtyzma
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... al to improve any neural video codec. On UVG, HEVC, and Xiph datasets, our codec improves the performance of a low-latency scale-space flow model by between 21% and 26% BD-rate savings, and that of a state-of-the-art B-frame model by 17 to 20% BD-rate savings. We also demonstrate that instance-adaptive finetuning improves the robustness to domain shift. Finally, our approach reduces the capacity requirements on compression models. We show that it enables a state-of-the-art performance even after reducing the network size by 72%.
Symmetry restored in dibosons at the LHC?
2015
Journal of High Energy Physics
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A number of LHC resonance search channels display an excess in the invariant mass region of 1.8-2.0 TeV. Among them is a 3.4 σ excess in the fully hadronic decay of a pair of Standard Model electroweak gauge bosons, in addition to potential signals in the HW and dijet final states. We perform a model-independent cross-section fit to the results of all ATLAS and CMS searches sensitive to these final states. We then interpret these results in the context of the Left-Right Symmetric Model, based
doi:10.1007/jhep10(2015)182
fatcat:3qhkzt2dqrd7nipeqlthhfzcme
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... the extended gauge group SU(2) L × SU(2) R × U(1) , and show that a heavy right-handed gauge boson W R can naturally explain the current measurements with just a single coupling g R ∼ 0.4. In addition, we discuss a possible connection to dark matter.
Flows for simultaneous manifold learning and density estimation
[article]
2020
arXiv
pre-print
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0.0 - 0.17 ± 1.18 PIE 139.5 ± 5.0 5539 ± 56 32.2 ± 0.8 4155 ± 31 −6.40 ± 1.54 M-flow 43.9 ± 0.2 332 ± 9 20.8 ± 0.5 1430 ± 4 2.67 ± 0.27 Me-flow 43.5 ± 0.2 303 ± 4 23.7 ± 0.2 1555 ± 3 1.81 ± 0.70
Brehmer ...
arXiv:2003.13913v3
fatcat:5vxteboyh5evndwsh2fspn3tzq
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