Multi-task learning for sequential data via iHMMs and the nested Dirichlet process

By Kai Ni, Lawrence Carin, David B. Dunson. 2007

26 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Björn Schuller
2. Qiang Yang
3. Anton Batliner
4. Stefan Steidl
5. Yu Zhang
6. Laurence Y. Devillers
7. Shrikanth S. Narayanan
8. Felix Burkhardt

Citations in the corpus

• A Survey on Multi-Task Learning (2017, methodology): “Moreover, a nested Dirichlet process is proposed in [62], [63] to use Dirichlet processes to learn both task clusters and the state structure of an infinite hidden Markov model, which handles sequential data in each task.”
• Paralinguistics in speech and language - State-of-the-art and the challenge (2013, methodology): “Further, Ni et al. (2007) propose a hierarchical Bayesian odel for multi-task learning with sequential data, and Roy and Kaelbling (2007) presents a hierarchical extension of he classic Naive Bayes classifier, coupling multiple Naive Bayes classifiers for multi-task learning.”