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Jeongin Yun

2 accepted papers

2026

LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers

AAAI 2026technical

How can we accurately quantize a pre-trained Vision Transformer model? Quantization algorithms compress Vision Transformers (ViTs) into low-bit formats, reducing memory and computation demands with minimal accuracy degradation. However, existing methods rely on uniform precision, ignoring the divers

Cited by 0SourcePDFScholar
2020

FleXOR: Trainable Fractional Quantization

NeurIPS 2020poster

Quantization based on the binary codes is gaining attention because each quantized bit can be directly utilized for computations without dequantization using look-up tables. Previous attempts, however, only allow for integer numbers of quantization bits, which ends up restricting the search space fo…

Cited by 15SourcePDFScholar