Smooth activations and reproducibility in deep networks

20 October 2020

Papers citing "Smooth activations and reproducibility in deep networks"

10 / 10 papers shown

Title
Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions Siqiao Mu Diego Klabjan MU 50 3 0 15 Sep 2024
Deterministic Nonsmooth Nonconvex Optimization Michael I. Jordan Guy Kornowski Tianyi Lin Ohad Shamir Manolis Zampetakis 49 24 0 16 Feb 2023
Instability in clinical risk stratification models using deep learning D. Martinez A. Yakubovich Martin G. Seneviratne Á. Lelkes Akshit Tyagi ... N. L. Downing Ron C. Li Keith Morse N. Shah Ming-Jun Chen OOD 27 2 0 20 Nov 2022
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models Rohan Anil S. Gadanho Danya Huang Nijith Jacob Zhuoshu Li ... Cristina Pop Kevin Regan G. Shamir Rakesh Shivanna Qiqi Yan 3DV 16 41 0 12 Sep 2022
Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments Christopher Hidey Fei Liu Rahul Goel 19 4 0 10 Apr 2022
Randomness In Neural Network Training: Characterizing The Impact of Tooling Donglin Zhuang Xingyao Zhang S. Song Sara Hooker 25 75 0 22 Jun 2021
Anti-Distillation: Improving reproducibility of deep networks G. Shamir Lorenzo Coviello 34 20 0 19 Oct 2020
TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks Xinyu Liu Xiaoguang Di 19 59 0 22 Mar 2020
Large scale distributed neural network training through online distillation Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton FedML 272 404 0 09 Apr 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016