Search Results for author:
Found 9 papers, 1 papers with code
Bridging Music and Text with Crowdsourced Music Comments: A Sequence-to-Sequence Framework for Thematic Music Comments Generation
We consider a novel task of automatically generating text descriptions of music.
Thompson Sampling for Bandit Learning in Matching Markets
The problem of two-sided matching markets has a wide range of real-world applications and has been extensively studied in the literature.
Deep Reinforcement Learning for Personalized Search Story Recommendation
In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms.
Multi-Label Annotation Aggregation in Crowdsourcing
As a means of human-based computation, crowdsourcing has been widely used to annotate large-scale unlabeled datasets.
Convex-constrained Sparse Additive Modeling and Its Extensions
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression.
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting
We propose a distributed approach to train deep neural networks (DNNs), which has guaranteed convergence theoretically and great scalability empirically: close to 6 times faster on instance of ImageNet data set when run with 6 machines.
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization
We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots.
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
We propose a scalable approach for making inference about latent spaces of large networks.
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks
A triangular motif is a vertex triple containing 2 or 3 edges, and the number of such motifs is $\Theta(\sum_{i}D_{i}^{2})$ (where $D_i$ is the degree of vertex $i$), which is much smaller than $N^2$ for low-maximum-degree networks.
