
-
Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann dist...
read it
-
Hierarchical clustering in particle physics through reinforcement learning
Particle physics experiments often require the reconstruction of decay p...
read it
-
Semi-parametric γ-ray modeling with Gaussian processes and variational inference
Mismodeling the uncertain, diffuse emission of Galactic origin can serio...
read it
-
Simulation-based inference methods for particle physics
Our predictions for particle physics processes are realized in a chain o...
read it
-
Sampling using SU(N) gauge equivariant flows
We develop a flow-based sampling algorithm for SU(N) lattice gauge theor...
read it
-
Discovering Symbolic Models from Deep Learning with Inductive Biases
We develop a general approach to distill symbolic representations of a l...
read it
-
Flows for simultaneous manifold learning and density estimation
We introduce manifold-modeling flows (MFMFs), a new class of generative ...
read it
-
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for ...
read it
-
Compact Representation of Uncertainty in Hierarchical Clustering
Hierarchical clustering is a fundamental task often used to discover mea...
read it
-
Set2Graph: Learning Graphs From Sets
Many problems in machine learning (ML) can be cast as learning functions...
read it
-
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
read it
-
The frontier of simulation-based inference
Many domains of science have developed complex simulations to describe p...
read it
-
Hamiltonian Graph Networks with ODE Integrators
We introduce an approach for imposing physically informed inductive bias...
read it
-
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
The subtle and unique imprint of dark matter substructure on extended ar...
read it
-
MadMiner: Machine learning-based inference for particle physics
The legacy measurements of the LHC will require analyzing high-dimension...
read it
-
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
read it
-
Effective LHC measurements with matrix elements and machine learning
One major challenge for the legacy measurements at the LHC is that the l...
read it
-
Inferring the quantum density matrix with machine learning
We introduce two methods for estimating the density matrix for a quantum...
read it
-
Likelihood-free inference with an improved cross-entropy estimator
We extend recent work (Brehmer, et. al., 2018) that use neural networks ...
read it
-
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel framework that enables efficient probabilistic infere...
read it
-
Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
read it
-
Backdrop: Stochastic Backpropagation
We introduce backdrop, a flexible and simple-to-implement method, intuit...
read it
-
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena; h...
read it
-
A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constra...
read it
-
Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field...
read it
-
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
read it
-
Adversarial Variational Optimization of Non-Differentiable Simulators
Complex computer simulators are increasingly used across fields of scien...
read it
-
QCD-Aware Recursive Neural Networks for Jet Physics
Recent progress in applying machine learning for jet physics has been bu...
read it
-
Learning to Pivot with Adversarial Networks
Several techniques for domain adaptation have been proposed to account f...
read it
-
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
In many fields of science, generalized likelihood ratio tests are establ...
read it