Jaan Altosaar

Noisin: Unbiased Regularization for Recurrent Neural Networks

2018 · arXiv (Cornell University)

Recurrent neural networks (RNNs) are powerful models of sequential data. They have been successfully used in domains such as text and speech. However, RNNs are susceptible to overfitting; regularization is important. In this paper we …

Factorization Meets the Item Embedding

2016

Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, …