Anti-Distillation: Improving reproducibility of deep networks

19 October 2020

Papers citing "Anti-Distillation: Improving reproducibility of deep networks"

10 / 10 papers shown

Title
A Tale of Two Imperatives: Privacy and Explainability Supriya Manna Niladri Sett 91 0 0 30 Dec 2024
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization Liang Zhang Junchi Yang Amin Karbasi Niao He 24 2 0 26 Oct 2023
Similarity of Neural Network Models: A Survey of Functional and Representational Measures Max Klabunde Tobias Schumacher M. Strohmaier Florian Lemmerich 52 64 0 10 May 2023
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
On the Prediction Instability of Graph Neural Networks Max Klabunde Florian Lemmerich 40 5 0 20 May 2022
Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments Christopher Hidey Fei Liu Rahul Goel 21 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
Knowledge Distillation by On-the-Fly Native Ensemble Xu Lan Xiatian Zhu S. Gong 192 473 0 12 Jun 2018
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