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Found 11 papers, 5 papers with code
Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds
Our experiments on datasets with up to one million features demonstrate the ability of our approach to generalize well despite the high dimensionality as well as its superiority compared to several competing methods.
Learn to Combine Modalities in Multimodal Deep Learning
Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches.
A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes
Our ablation experiments demonstrate the effectiveness of the two components to address heterogeneous attribute challenges including variable lengths and attribute sparseness.
A Batch Learning Framework for Scalable Personalized Ranking
In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list.
WMRB: Learning to Rank in a Scalable Batch Training Approach
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).
Kernel Approximation Methods for Speech Recognition
First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection.
Temporal Learning and Sequence Modeling for a Job Recommender System
We present our solution to the job recommendation task for RecSys Challenge 2016.
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition.
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Similarity Learning for High-Dimensional Sparse Data
A good measure of similarity between data points is crucial to many tasks in machine learning.
Similarity Component Analysis
Moreover, we show how SCA can be instrumental in exploratory analysis of data, where we gain insights about the data by examining patterns hidden in its latent components' local similarity values.
