Michael Kagan | DeepAI

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∙ 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...

0 Michael Kagan, et al. ∙

∙ 08/05/2022

Interpretable Uncertainty Quantification in AI for HEP

Estimating uncertainty is at the core of performing scientific measureme...

0 Thomas Y. Chen, et al. ∙

∙ 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 ...

0 Elham E Khoda, et al. ∙

∙ 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 ...

0 Sanha Cheong, et al. ∙

∙ 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...

297 Savannah Thais, et al. ∙

∙ 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...

0 Andreas Adelmann, et al. ∙

∙ 02/28/2022

Differentiable Matrix Elements with MadJax

MadJax is a tool for generating and evaluating differentiable matrix ele...

0 Lukas Heinrich, et al. ∙

∙ 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...

6 Youssef S. G. Nashed, et al. ∙

∙ 12/17/2020

Image-Based Jet Analysis

Image-based jet analysis is built upon the jet image representation of j...

0 Michael Kagan, et al. ∙

∙ 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...

11 Maxime Vandegar, et al. ∙

∙ 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...

22 Sergey Shirobokov, et al. ∙

∙ 03/11/2019

Continual Learning via Neural Pruning

We introduce Continual Learning via Neural Pruning (CLNP), a new method ...

14 Siavash Golkar, et al. ∙

∙ 07/08/2018

Machine Learning in High Energy Physics Community White Paper

Machine learning is an important research area in particle physics, begi...

0 Kim Albertsson, et al. ∙

∙ 11/03/2016

Learning to Pivot with Adversarial Networks

Several techniques for domain adaptation have been proposed to account f...

0 Gilles Louppe, et al. ∙

∙ 11/16/2015

Jet-Images -- Deep Learning Edition

Building on the notion of a particle physics detector as a camera and th...

0 Luke de Oliveira, et al. ∙

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