Regularization of context-dependent deep neural networks with context-independent multi-task training

By Peter Bell, S. Renals. 2015

50 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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. Qiang Yang
2. Yu Zhang
3. Leonardo Badino
4. Mingsheng Long
5. Jianmin Wang
6. Dongrui Wu
7. Sinno Jialin Pan
8. Sebastian Ruder

In Signal Processing

1. Qiang Yang
2. Leonardo Badino
3. Yu Zhang
4. Yu Zheng
5. Mingsheng Long
6. Jianmin Wang
7. Sinno Jialin Pan

Citations in the corpus

• A Survey on Multi-Task Learning (2017, methodology): “Feature Transformation Visual tracking [192], [193] Protein subcellular location prediction [194] Speech synthesis [195], [196] Learning to rank [197] Stock prediction [198] Action recognition [199] Protein interaction prediction [200] Speech recognition [201], [202] Localization [203] Facial landma”
• Phonetic Context Embeddings for DNN-HMM Phone Recognition (2016, background): “, triphone states) are assigned the same probability even when their frequency can be very different, and the “equality problem” where same-monophone senones and different-mononophone senones are equally discriminated by the DNN [6, 7].”