Search Results for author: Jong-Seon No

Found 5 papers, 1 papers with code

CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes

no code implementations2 May 2024 Seong-Joon Park, Hee-Youl Kwak, Sang-Hyo Kim, Yongjune Kim, Jong-Seon No

The mask matrices in these cross-attention blocks are determined by the code's parity-check matrix that delineates the relationship between magnitude and syndrome vectors.

Decoder

Optimizing Layerwise Polynomial Approximation for Efficient Private Inference on Fully Homomorphic Encryption: A Dynamic Programming Approach

no code implementations16 Oct 2023 Junghyun Lee, Eunsang Lee, Young-Sik Kim, Yongwoo Lee, Joon-Woo Lee, Yongjune Kim, Jong-Seon No

Unlike the previous works approximating activation functions uniformly and conservatively, this paper presents a \emph{layerwise} degree optimization of activation functions to aggressively reduce the inference time while maintaining classification accuracy by taking into account the characteristics of each layer.

Privacy Preserving

Boosting Learning for LDPC Codes to Improve the Error-Floor Performance

1 code implementation NeurIPS 2023 Hee-Youl Kwak, Dae-Young Yun, Yongjune Kim, Sang-Hyo Kim, Jong-Seon No

The proposed NMS decoder, optimized solely through novel training methods without additional modules, can be integrated into existing LDPC decoders without incurring extra hardware costs.

Decoder

How to Mask in Error Correction Code Transformer: Systematic and Double Masking

no code implementations16 Aug 2023 Seong-Joon Park, Hee-Youl Kwak, Sang-Hyo Kim, Sunghwan Kim, Yongjune Kim, Jong-Seon No

In communication and storage systems, error correction codes (ECCs) are pivotal in ensuring data reliability.

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

no code implementations14 Jun 2021 Joon-Woo Lee, HyungChul Kang, Yongwoo Lee, Woosuk Choi, Jieun Eom, Maxim Deryabin, Eunsang Lee, Junghyun Lee, Donghoon Yoo, Young-Sik Kim, Jong-Seon No

Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations.

BIG-bench Machine Learning Privacy Preserving

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