133 citations · 294 across the 9 of their papers we have counts for
10 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…
A Batch Learning Framework for Scalable Personalized Ranking
Kuan Liu, Prem Natarajan
In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list. Existing methods either seek a smooth convex surrogate fo…
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…
Kernel Approximation Methods for Speech Recognition
Avner May, Alireza Bagheri Garakani, Zhiyun Lu +9
We study large-scale kernel methods for acoustic modeling in speech recognition and compare their performance to deep neural networks (DNNs). We perform experiments on four speech…
Temporal Learning and Sequence Modeling for a Job Recommender System
Kuan Liu, Xing Shi, Anoop Kumar +2
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to captur…
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…
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
Zhiyun Lu, Avner May, Kuan Liu +8
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems. We argue that this barrier can be effectively over…
Similarity Learning for High-Dimensional Sparse Data
Kuan Liu, Aurélien Bellet, Fei Sha
A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, b…