1.1k citations · 2.1k across the 27 of their papers we have counts for
30 papers
Differentiable and Learnable Wireless Simulation with Geometric Transformers
Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy +2
Modelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems. Wireless ray tracing simulators model signal propagation based…
Geometric Algebra Transformer
Johann Brehmer, Pim de Haan, Sönke Behrends +1
Problems involving geometric data arise in physics, chemistry, robotics, computer vision, and many other fields. Such data can take numerous forms, for instance points, direction v…
EDGI: Equivariant Diffusion for Planning with Embodied Agents
Johann Brehmer, Joey Bose, Pim de Haan +1
Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement le…
Deconfounding Imitation Learning with Variational Inference
Risto Vuorio, Pim de Haan, Johann Brehmer +3
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent. This is because partial observability gives rise to hidde…
Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe +1
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under…
Implicit Neural Video Compression
Yunfan Zhang, Ties van Rozendaal, Johann Brehmer +2
We propose a method to compress full-resolution video sequences with implicit neural representations. Each frame is represented as a neural network that maps coordinate positions t…
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
Ties van Rozendaal, Johann Brehmer, Yunfan Zhang +3
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 pa…
Back to the Formula -- LHC Edition
Anja Butter, Tilman Plehn, Nathalie Soybelman +1
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…
Hierarchical clustering in particle physics through reinforcement learning
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo +1
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…
Simulation-based inference methods for particle physics
Johann Brehmer, Kyle Cranmer
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 f…
Flows for simultaneous manifold learning and density estimation
Johann Brehmer, Kyle Cranmer
We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that mani…
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, Gilles Louppe
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 infer…
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans +2
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark…
Benchmarking simplified template cross sections in production
Johann Brehmer, Sally Dawson, Samuel Homiller +2
Simplified template cross sections define a framework for the measurement and dissemination of kinematic information in Higgs measurements. We benchmark the currently proposed setu…
MadMiner: Machine learning-based inference for particle physics
Johann Brehmer, Felix Kling, Irina Espejo +1
Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recent…
Effective LHC measurements with matrix elements and machine learning
Johann Brehmer, Kyle Cranmer, Irina Espejo +3
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 ha…
Likelihood-free inference with an improved cross-entropy estimator
Markus Stoye, Johann Brehmer, Gilles Louppe +2
We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joi…
Mining gold from implicit models to improve likelihood-free inference
Johann Brehmer, Gilles Louppe, Juan Pavez +1
Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often i…
A Guide to Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics p…
Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra informatio…
Better Higgs-CP Tests Through Information Geometry
Johann Brehmer, Felix Kling, Tilman Plehn +1
Measuring the CP symmetry in the Higgs sector is one of the key tasks of the LHC and a crucial ingredient for precision studies, for example in the language of effective Lagrangian…
Better Higgs Measurements Through Information Geometry
Johann Brehmer, Kyle Cranmer, Felix Kling +1
Information geometry can be used to understand and optimize Higgs measurements at the LHC. The Fisher information encodes the maximum sensitivity of observables to model parameters…
Les Houches 2015: Physics at TeV colliders - new physics working group report
G. Brooijmans, C. Delaunay, A. Delgado +78
We present the activities of the 'New Physics' working group for the 'Physics at TeV Colliders' workshop (Les Houches, France, 1-19 June, 2015). Our report includes new physics stu…
Pushing Higgs Effective Theory over the Edge
Anke Biekötter, Johann Brehmer, Tilman Plehn
Based on a vector triplet model we study a possible failure of dimension-6 operators in describing LHC Higgs kinematics. First, we illustrate that including dimension-6 contributio…