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Found 7 papers, 1 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.
Nonlinear Initialization Methods for Low-Rank Neural Networks
We propose a novel low-rank initialization framework for training low-rank deep neural networks -- networks where the weight parameters are re-parameterized by products of two low-rank matrices.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Learning effective feature crosses is the key behind building recommender systems.
Ranked #3 on
Click-Through Rate Prediction
on Criteo
Understanding and Improving Knowledge Distillation
Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget.
How Many Pairwise Preferences Do We Need to Rank A Graph Consistently?
Our proposed algorithm, {\it Pref-Rank}, predicts the underlying ranking using an SVM based approach over the chosen embedding of the product graph, and is the first to provide \emph{statistical consistency} on two ranking losses: \emph{Kendall's tau} and \emph{Spearman's footrule}, with a required sample complexity of $O(n^2 \chi(\bar{G}))^{\frac{2}{3}}$ pairs, $\chi(\bar{G})$ being the \emph{chromatic number} of the complement graph $\bar{G}$.
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
We propose an alternative PAC-based bound, which do not depend on the VC dimension of the underlying function class, but is related to the famous Lov\'{a}sz~$\vartheta$ function.
Learning on graphs using Orthonormal Representation is Statistically Consistent
This, for the first time, relates labelled sample complexity to graph connectivity properties, such as the density of graphs.
