Jun Wang

TL;DR: SeqGAN as mentioned in this paper models the data generator as a stochastic policy in reinforcement learning (RL), and the RL reward signal comes from the discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search.

TL;DR: A novel kernel-based supervised hashing model which requires a limited amount of supervised information, i.e., similar and dissimilar data pairs, and a feasible training cost in achieving high quality hashing, and significantly outperforms the state-of-the-arts in searching both metric distance neighbors and semantically similar neighbors is proposed.

TL;DR: This paper proposes a novel graph-based hashing method which automatically discovers the neighborhood structure inherent in the data to learn appropriate compact codes and describes a hierarchical threshold learning procedure in which each eigenfunction yields multiple bits, leading to higher search accuracy.

TL;DR: This work proposes a semi-supervised hashing (SSH) framework that minimizes empirical error over the labeled set and an information theoretic regularizer over both labeled and unlabeled sets and presents three different semi- supervised hashing methods, including orthogonal hashing, nonorthogonal hash, and sequential hashing.

TL;DR: This paper re-formulates the memory-based collaborative filtering problem in a generative probabilistic framework, treating individual user-item ratings as predictors of missing ratings and shows that the proposed methods are indeed more robust against data sparsity and give better recommendations.