4 citations · 4 across the 1 of their papers we have counts for
5 papers
Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds
Kuan Liu, Aurélien Bellet
Similarity and metric learning provides a principled approach to construct a task-specific similarity from weakly supervised data. However, these methods are subject to the curse o…
Learn to Combine Modalities in Multimodal Deep Learning
Kuan Liu, Yanen Li, Ning Xu +1
Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches. However, it is challenging to full…
A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes
Kuan Liu, Xing Shi, Prem Natarajan
Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty…
WMRB: Learning to Rank in a Scalable Batch Training Approach
Kuan Liu, Prem Natarajan
We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP). WMRB uses a new ran…
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani +8
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and fra…