567 citations · 730 across the 11 of their papers we have counts for
11 papers
Learning to Rank when Grades Matter
Le Yan, Zhen Qin, Gil Shamir +3
Graded labels are ubiquitous in real-world learning-to-rank applications, especially in human rated relevance data. Traditional learning-to-rank techniques aim to optimize the rank…
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
Rohan Anil, Sandra Gadanho, Da Huang +9
For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central problem. Ad clicks constitute a significant class of user engagements and are often…
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…
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Haichao Yu, Zhe Chen, Dong Lin +2
Dropout has been commonly used to quantify prediction uncertainty, i.e, the variations of model predictions on a given input example. However, using dropout in practice can be expe…
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…
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
Lei Ding, Kai Zheng, Dong Lin +4
There are limited studies on the semantic segmentation of high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images due to the scarcity of training data and the inferen…