143 citations · 351 across the 12 of their papers we have counts for
12 papers
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Ting-Yao Hu, Ashish Shrivastava, Jen-Hao Rick Chang +5
Data augmentation methods usually apply the same augmentation (or a mix of them) to all the training samples. For example, to perturb data with noise, the noise is sampled from a N…
Quantized neural network design under weight capacity constraint
Sungho Shin, Kyuyeon Hwang, Wonyong Sung
The complexity of deep neural network algorithms for hardware implementation can be lowered either by scaling the number of units or reducing the word-length of weights. Both appro…
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
Minjae Lee, Kyuyeon Hwang, Jinhwan Park +3
In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent…
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks
Kyuyeon Hwang, Wonyong Sung
Recurrent neural network (RNN) based character-level language models (CLMs) are extremely useful for modeling out-of-vocabulary words by nature. However, their performance is gener…
Generative Knowledge Transfer for Neural Language Models
Sungho Shin, Kyuyeon Hwang, Wonyong Sung
In this paper, we propose a generative knowledge transfer technique that trains an RNN based language model (student network) using text and output probabilities generated from a p…
Character-Level Incremental Speech Recognition with Recurrent Neural Networks
Kyuyeon Hwang, Wonyong Sung
In real-time speech recognition applications, the latency is an important issue. We have developed a character-level incremental speech recognition (ISR) system that responds quick…
Online Keyword Spotting with a Character-Level Recurrent Neural Network
Kyuyeon Hwang, Minjae Lee, Wonyong Sung
In this paper, we propose a context-aware keyword spotting model employing a character-level recurrent neural network (RNN) for spoken term detection in continuous speech. The RNN…
Structured Pruning of Deep Convolutional Neural Networks
Sajid Anwar, Kyuyeon Hwang, Wonyong Sung
Real time application of deep learning algorithms is often hindered by high computational complexity and frequent memory accesses. Network pruning is a promising technique to solve…
Fixed-Point Performance Analysis of Recurrent Neural Networks
Sungho Shin, Kyuyeon Hwang, Wonyong Sung
Recurrent neural networks have shown excellent performance in many applications, however they require increased complexity in hardware or software based implementations. The hardwa…
Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification
Kyuyeon Hwang, Wonyong Sung
Connectionist temporal classification (CTC) based supervised sequence training of recurrent neural networks (RNNs) has shown great success in many machine learning areas including…
Resiliency of Deep Neural Networks under Quantization
Wonyong Sung, Sungho Shin, Kyuyeon Hwang
The complexity of deep neural network algorithms for hardware implementation can be much lowered by optimizing the word-length of weights and signals. Direct quantization of floati…
Single stream parallelization of generalized LSTM-like RNNs on a GPU
Kyuyeon Hwang, Wonyong Sung
Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data. However, they suffer from long training time, which demands parallel implementation…