Search Results for author: Jianbo Ye

Found 13 papers, 5 papers with code

ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild

no code implementations28 Aug 2018 Yu Luo, Jianbo Ye, Reginald B. Adams, Jr., Jia Li, Michelle G. Newman, James Z. Wang

A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated.

Action Recognition

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

1 code implementation ICLR 2018 Jianbo Ye, Xin Lu, Zhe Lin, James Z. Wang

Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios.

A Faster Drop-in Implementation for Leaf-wise Exact Greedy Induction of Decision Tree Using Pre-sorted Deque

1 code implementation19 Dec 2017 Jianbo Ye

This short article presents a new implementation for decision trees.

Data Structures and Algorithms

Aggregated Wasserstein Metric and State Registration for Hidden Markov Models

no code implementations12 Nov 2017 Yukun Chen, Jianbo Ye, Jia Li

This distance quantifies the dissimilarity of GMM-HMMs by measuring both the difference between the two marginal GMMs and that between the two transition matrices.

Time Series

Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data

no code implementations4 Jan 2017 Jianbo Ye, Jia Li, Michelle G. Newman, Reginald B. Adams, Jr., James Z. Wang

We proposed a probabilistic approach to joint modeling of participants' reliability and humans' regularity in crowdsourced affective studies.

Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support

2 code implementations30 Sep 2015 Jianbo Ye, Panruo Wu, James Z. Wang, Jia Li

In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects.

Multiple Closed-Form Local Metric Learning for K-Nearest Neighbor Classifier

no code implementations12 Nov 2013 Jianbo Ye

Many researches have been devoted to learn a Mahalanobis distance metric, which can effectively improve the performance of kNN classification.

General Classification Metric Learning

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