28 citations · 60 across the 7 of their papers we have counts for
8 papers
One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations
Ramin Raziperchikolaei, Young-joo Chung
In one-class recommendation systems, the goal is to learn a model from a small set of interacted users and items and then identify the positively-related user-item pairs among a la…
Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering
Ramin Raziperchikolaei, Young-joo Chung
Neural network-based collaborative filtering systems focus on designing network architectures to learn better representations while fixing the input to the user/item interaction ve…
Estimating Vector Fields from Noisy Time Series
Harish S. Bhat, Majerle Reeves, Ramin Raziperchikolaei
While there has been a surge of recent interest in learning differential equation models from time series, methods in this area typically cannot cope with highly noisy data. We bre…
Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization
Ramin Raziperchikolaei, Tianyu Li, Young-joo Chung
Autoencoder-based hybrid recommender systems have become popular recently because of their ability to learn user and item representations by reconstructing various information sour…
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
Ramin Raziperchikolaei, Harish S. Bhat
We propose and analyze a block coordinate descent proximal algorithm (BCD-prox) for simultaneous filtering and parameter estimation of ODE models. As we show on ODE systems with up…
An ensemble diversity approach to supervised binary hashing
Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei
Binary hashing is a well-known approach for fast approximate nearest-neighbor search in information retrieval. Much work has focused on affinity-based objective functions involving…
Hashing with binary autoencoders
Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the…
Optimizing affinity-based binary hashing using auxiliary coordinates
Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán
In supervised binary hashing, one wants to learn a function that maps a high-dimensional feature vector to a vector of binary codes, for application to fast image retrieval. This t…