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Found 8 papers, 2 papers with code
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central problem.
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
We describe a novel family of smooth activations; Smooth ReLU (SmeLU), designed to improve reproducibility with mathematical simplicity, with potentially cheaper implementation.
Reproducibility in Optimization: Theoretical Framework and Limits
We initiate a formal study of reproducibility in optimization.
Synthesizing Irreproducibility in Deep Networks
We show that even with a single nonlinearity and for very simple data and models, irreproducibility occurs.
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning
Various approximations that, for huge sparse feature sets, diminish the theoretical advantages, must be used.
Smooth activations and reproducibility in deep networks
We propose a new family of activations; Smooth ReLU (\emph{SmeLU}), designed to give such better tradeoffs, while also keeping the mathematical expression simple, and thus implementation cheap.
Anti-Distillation: Improving reproducibility of deep networks
Deep networks have been revolutionary in improving performance of machine learning and artificial intelligence systems.
Logistic Regression Regret: What's the Catch?
We derive lower bounds with logarithmic regret under $L_1$, $L_2$, and $L_\infty$ constraints on the parameter values.
