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Found 8 papers, 4 papers with code
One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations
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 large number of pairs with unknown interactions.
Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering
Then, we propose to learn the value of the non-zero elements of the inputs jointly with the neural network parameters.
Estimating Vector Fields from Noisy Time Series
We also study the combination of either our neural shape function method or existing differential equation learning methods with alternating minimization and multiple trajectories.
Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization
We also apply the NRP framework to a direct neural network structure which predicts the ratings without reconstructing the user and item information.
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
We propose and analyze a block coordinate descent proximal algorithm (BCD-prox) for simultaneous filtering and parameter estimation of ODE models.
An ensemble diversity approach to supervised binary hashing
They ensure that the hash functions differ from each other through constraints or penalty terms that encourage codes to be orthogonal or dissimilar across bits, but this couples the binary variables and complicates the already difficult optimization.
Optimizing affinity-based binary hashing using auxiliary coordinates
Recent work has tried to optimize the objective directly over the binary codes and achieved better results, but the hash function was still learned a posteriori, which remains suboptimal.
Hashing with binary autoencoders
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 search is done in this binary space.
