8 citations · 21 across the 5 of their papers we have counts for
5 papers · 1 filter
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
Gil I. Shamir, Dong Lin
Real world recommendation systems influence a constantly growing set of domains. With deep networks, that now drive such systems, recommendations have been more relevant to the use…
Smooth activations and reproducibility in deep networks
Gil I. Shamir, Dong Lin, Lorenzo Coviello
Deep networks are gradually penetrating almost every domain in our lives due to their amazing success. However, with substantive performance accuracy improvements comes the price o…
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Zhe Chen, Yuyan Wang, Dong Lin +4
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification an…
Small Towers Make Big Differences
Yuyan Wang, Zhe Zhao, Bo Dai +4
Multi-task learning aims at solving multiple machine learning tasks at the same time. A good solution to a multi-task learning problem should be generalizable in addition to being…
Understanding and Improving Knowledge Distillation
Jiaxi Tang, Rakesh Shivanna, Zhe Zhao +4
Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget. It is a commonly used technique for model compression, wher…