research
∙
08/31/2023
Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics
We propose to apply several gradient estimation techniques to enable the...
share
research
∙
08/05/2022
Interpretable Uncertainty Quantification in AI for HEP
Estimating uncertainty is at the core of performing scientific measureme...
research
∙
07/01/2022
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Recurrent neural networks have been shown to be effective architectures ...
research
∙
05/23/2022
Novel Light Field Imaging Device with Enhanced Light Collection for Cold Atom Clouds
We present a light field imaging system that captures multiple views of ...
research
∙
03/23/2022
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Many physical systems can be best understood as sets of discrete data wi...
research
∙
03/15/2022
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in fu...
research
∙
02/28/2022
Differentiable Matrix Elements with MadJax
MadJax is a tool for generating and evaluating differentiable matrix ele...
research
∙
07/07/2021
End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data
Cryogenic electron microscopy (cryo-EM) provides images from different c...
research
∙
12/17/2020
Image-Based Jet Analysis
Image-based jet analysis is built upon the jet image representation of j...
research
∙
11/11/2020
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
We revisit empirical Bayes in the absence of a tractable likelihood func...
research
∙
02/11/2020
Differentiating the Black-Box: Optimization with Local Generative Surrogates
We propose a novel method for gradient-based optimization of black-box s...
research
∙
03/11/2019
Continual Learning via Neural Pruning
We introduce Continual Learning via Neural Pruning (CLNP), a new method ...
research
∙
07/08/2018
Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
research
∙
11/03/2016
Learning to Pivot with Adversarial Networks
Several techniques for domain adaptation have been proposed to account f...
research
∙
11/16/2015
