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20212024
most citedA Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition

2 citations · 4 across the 5 of their papers we have counts for

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5 papers

cs.CL2024

InfiniPot: Infinite Context Processing on Memory-Constrained LLMs

Minsoo Kim, Kyuhong Shim, Jungwook Choi +1

Handling long input contexts remains a significant challenge for Large Language Models (LLMs), particularly in resource-constrained environments such as mobile devices. Our work ai…

eess.AS20222 cited

A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition

Kyuhong Shim, Wonyong Sung

Phoneme recognition is a very important part of speech recognition that requires the ability to extract phonetic features from multiple frames. In this paper, we compare and analyz…

eess.SP20221 cited

Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming

Yongjun Ahn, Jinhong Kim, Seungnyun Kim +4

Beamforming technique realized by the multiple-input-multiple-output (MIMO) antenna arrays has been widely used to compensate for the severe path loss in the millimeter wave (mmWav…

cs.CL2022

Korean Tokenization for Beam Search Rescoring in Speech Recognition

Kyuhong Shim, Hyewon Bae, Wonyong Sung

The performance of automatic speech recognition (ASR) models can be greatly improved by proper beam-search decoding with external language model (LM). There has been an increasing…

cs.CL20211 cited

Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling

Kyuhong Shim, Iksoo Choi, Wonyong Sung +1

While Transformer-based models have shown impressive language modeling performance, the large computation cost is often prohibitive for practical use. Attention head pruning, which…