Sort by:

• Highly Influential Citations
• Citations
• Acceleration
• Velocity
• Recency

Learn More

Audio Word2Vec: Unsupervised Learning of Audio Segment Representations Using Sequence-to-Sequence Autoencoder

• Yu-An Chung, Chao-Chung Wu, Chia-Hao Shen, Hung-yi Lee, Lin-Shan Lee
• INTERSPEECH
• 2016

The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the semantic information they carry. This paper proposes a parallel version, the Audio Word2Vec. It offers the vector representations of fixed dimensionality for variable-length… (More)

Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning

• Yu-An Chung, Hsuan-Tien Lin, Shao-Wen Yang
• IJCAI
• 2016

Deep learning has been one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications where automatic feature extraction is needed. Many such applications also demand varying costs for different types of mis-classification errors, but it is not clear whether or how such cost information can be… (More)

High-level energy consumption model of embedded graphic processors

• Chen-Wei Huang, Yu-An Chung, Pei-Shu Huang, Shiao-Li Tsao
• 2015 IEEE International Conference on Digital…
• 2015

Embedded graphic processing unit (GPU) is an indispensable component in enabling real-time rendering and graphic applications on mobile devices. However, embedded GPU consumes a considerable energy [1] which is critical for battery-operated devices. To understand the energy consumption of a graphic application, conventional approaches suggested the energy… (More)

Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

• Yu-An Chung, Hsuan-Tien Lin
• ArXiv
• 2016

While deep neural networks have succeeded in several visual applications, such as object recognition, detection, and local-ization, by reaching very high classification accuracies, it is important to note that many real-world applications demand varying costs for different types of misclassification errors, thus requiring cost-sensitive classification… (More)

• ‹
• 1
• ›