54 citations · 192 across the 14 of their papers we have counts for
14 papers
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Wenlin Wang, Hongteng Xu, Zhe Gan +6
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogen…
Learning to Recommend from Sparse Data via Generative User Feedback
Wenlin Wang, Hongteng Xu, Ruiyi Zhang +3
Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce. To address this, we propose a l…
Zero-Shot Recognition via Optimal Transport
Wenlin Wang, Hongteng Xu, Guoyin Wang +2
We propose an optimal transport (OT) framework for generalized zero-shot learning (GZSL), seeking to distinguish samples for both seen and unseen classes, with the assist of auxili…
Synthetic Data Generation and Adaption for Object Detection in Smart Vending Machines
Kai Wang, Fuyuan Shi, Wenqi Wang +2
This paper presents an improved scheme for the generation and adaption of synthetic images for the training of deep Convolutional Neural Networks(CNNs) to perform the object detect…
Principal Component Analysis with Tensor Train Subspace
Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
Tensor train is a hierarchical tensor network structure that helps alleviate the curse of dimensionality by parameterizing large-scale multidimensional data via a set of network of…
Wide Compression: Tensor Ring Nets
Wenqi Wang, Yifan Sun, Brian Eriksson +2
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger…
Topic Compositional Neural Language Model
Wenlin Wang, Zhe Gan, Wenqi Wang +5
We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local word ordering struc…
Tensor Train Neighborhood Preserving Embedding
Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to…
Efficient Low Rank Tensor Ring Completion
Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alterna…
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification
Wenlin Wang, Changyou Chen, Wenqi Wang +2
We present Earliness-Aware Deep Convolutional Networks (EA-ConvNets), an end-to-end deep learning framework, for early classification of time series data. Unlike most existing meth…
Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model
Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
In this paper, we consider clustering data that is assumed to come from one of finitely many pointed convex polyhedral cones. This model is referred to as the Union of Polyhedral C…
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model
Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
Using the matrix product state (MPS) representation of tensor train decompositions, in this paper we propose a tensor completion algorithm which alternates over the matrices (tenso…
On Deterministic Conditions for Subspace Clustering under Missing Data
Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal
In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) m…
On deterministic conditions for subspace clustering under missing data
Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal
In this paper we present deterministic analysis of sufficient conditions for sparse subspace clustering under missing data, when data is assumed to come from a Union of Subspaces (…