no code implementations • 6 Feb 2021 • Hyeongju Kim, Woo Hyun Kang, Hyeonseung Lee, Nam Soo Kim
Photoplethysmogram (PPG) signal-based blood pressure (BP) estimation is a promising candidate for modern BP measurements, as PPG signals can be easily obtained from wearable devices in a non-invasive manner, allowing quick BP measurement.
no code implementations • 3 Aug 2020 • Ji Won Yoon, Hyeonseung Lee, Hyung Yong Kim, Won Ik Cho, Nam Soo Kim
To reduce this computational burden, knowledge distillation (KD), which is a popular model compression method, has been used to transfer knowledge from a deep and complex model (teacher) to a shallower and simpler model (student).
no code implementations • 5 Nov 2021 • Ji Won Yoon, Hyung Yong Kim, Hyeonseung Lee, Sunghwan Ahn, Nam Soo Kim
Extending this supervised scheme further, we introduce a new type of teacher model for connectionist temporal classification (CTC)-based sequence models, namely Oracle Teacher, that leverages both the source inputs and the output labels as the teacher model's input.
no code implementations • 28 Nov 2022 • Ji Won Yoon, Beom Jun Woo, Sunghwan Ahn, Hyeonseung Lee, Nam Soo Kim
Recently, the advance in deep learning has brought a considerable improvement in the end-to-end speech recognition field, simplifying the traditional pipeline while producing promising results.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 14 Jun 2023 • Ji Won Yoon, Sunghwan Ahn, Hyeonseung Lee, Minchan Kim, Seok Min Kim, Nam Soo Kim
We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning.
1 code implementation • NeurIPS 2020 • Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, Nam Soo Kim
Flow-based generative models are composed of invertible transformations between two random variables of the same dimension.
Ranked #3 on Point Cloud Generation on ShapeNet Airplane
1 code implementation • 8 Jun 2020 • Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Sung Jun Cheon, Byoung Jin Choi, Nam Soo Kim
In recent years, various flow-based generative models have been proposed to generate high-fidelity waveforms in real-time.