ByeongWook Kim
ByeongWook Kim
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Structured compression by weight encryption for unstructured pruning and quantization
SJ Kwon, D Lee, B Kim, P Kapoor, B Park, GY Wei
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern íŽ, 2020
Deeptwist: Learning model compression via occasional weight distortion
D Lee, P Kapoor, B Kim
arXiv preprint arXiv:1810.12823, 2018
Biqgemm: matrix multiplication with lookup table for binary-coding-based quantized dnns
Y Jeon, B Park, SJ Kwon, B Kim, J Yun, D Lee
SC20: International Conference for High Performance Computing, Networking íŽ, 2020
Learning low-rank approximation for cnns
D Lee, SJ Kwon, B Kim, GY Wei
arXiv preprint arXiv:1905.10145, 2019
Extremely low bit transformer quantization for on-device neural machine translation
I Chung, B Kim, Y Choi, SJ Kwon, Y Jeon, B Park, S Kim, D Lee
arXiv preprint arXiv:2009.07453, 2020
Retraining-based iterative weight quantization for deep neural networks
D Lee, B Kim
arXiv preprint arXiv:1805.11233, 2018
nuqmm: Quantized matmul for efficient inference of large-scale generative language models
G Park, B Park, SJ Kwon, B Kim, Y Lee, D Lee
arXiv preprint arXiv:2206.09557, 2022
Flexor: Trainable fractional quantization
D Lee, SJ Kwon, B Kim, Y Jeon, B Park, J Yun
Advances in neural information processing systems 33, 1311-1321, 2020
Network pruning for low-rank binary indexing
D Lee, SJ Kwon, B Kim, P Kapoor, GY Wei
arXiv preprint arXiv:1905.05686, 2019
Computation-efficient quantization method for deep neural networks
P Kapoor, D Lee, B Kim, S Lee
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
SJ Kwon, J Kim, J Bae, KM Yoo, JH Kim, B Park, B Kim, JW Ha, N Sung, ...
arXiv preprint arXiv:2210.03858, 2022
Q-rater: Non-convex optimization for post-training uniform quantization
B Kim, D Lee, Y Ro, Y Jeon, SJ Kwon, B Park, D Oh
arXiv preprint arXiv:2105.01868, 2021
Post-training weighted quantization of neural networks for language models
SJ Kwon, D Lee, Y Jeon, B Kim, BS Park, Y Ro
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
B Park, SJ Kwon, D Oh, B Kim, D Lee
arXiv preprint arXiv:2105.01869, 2021
Modulating Regularization Frequency for Efficient Compression-Aware Model Training
D Lee, SJ Kwon, B Kim, J Yun, B Park, Y Jeon
arXiv preprint arXiv:2105.01875, 2021
Sequential Encryption of Sparse Neural Networks Toward Optimum Representation of Irregular Sparsity.
B Park, SJ Kwon, D Lee, D Oh, B Kim, Y Jeon, Y Ro
CoRR, 2021
Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks.
SJ Kwon, D Lee, B Kim, P Kapoor, B Park, GY Wei
CoRR, 2019
Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic
Y Kim, J Jang, J Lee, J Park, J Kim, B Kim, SJ Kwon, D Lee
The Eleventh International Conference on Learning Representations, 0
Network Pruning for Low-Rank Binary Index
D Lee, SJ Kwon, B Kim, P Kapoor, GY Wei
Decoupling Weight Regularization from Batch Size for Model Compression
D Lee, SJ Kwon, B Kim, Y Jeon, B Park, J Yun, GY Wei
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