Smoothing Methods for Automatic Differentiation Across Conditional Branches

5 October 2023

Papers citing "Smoothing Methods for Automatic Differentiation Across Conditional Branches"

8 / 8 papers shown

Title
Resource-efficient Inference with Foundation Model Programs Lunyiu Nie Zhimin Ding Kevin Yu Marco Cheung C. Jermaine S. Chaudhuri 22 0 0 09 Apr 2025
Probabilistic Programming with Programmable Variational Inference McCoy R. Becker Alexander K. Lew Xiaoyan Wang Matin Ghavami Mathieu Huot Martin Rinard Vikash K. Mansinghka 43 3 0 22 Jun 2024
Towards Learning Stochastic Population Models by Gradient Descent J. N. Kreikemeyer Philipp Andelfinger A. Uhrmacher 27 0 0 10 Apr 2024
Automatic Gradient Estimation for Calibrating Crowd Models with Discrete Decision Making Philipp Andelfinger J. N. Kreikemeyer 16 0 0 06 Apr 2024
Neurosymbolic Programming for Science Jennifer J. Sun Megan Tjandrasuwita Atharva Sehgal Armando Solar-Lezama Swarat Chaudhuri Yisong Yue Omar Costilla-Reyes NAI 32 12 0 10 Oct 2022
Bundled Gradients through Contact via Randomized Smoothing H. Suh Tao Pang Russ Tedrake 74 52 0 11 Sep 2021
Learning Differentiable Programs with Admissible Neural Heuristics Ameesh Shah Eric Zhan Jennifer J. Sun Abhinav Verma Yisong Yue Swarat Chaudhuri 126 43 0 23 Jul 2020
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 175 1,182 0 30 Nov 2014