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Jinseok Kim

7 accepted papers

2026

ReQAT: Achieving Full-Precision Reasoning Accuracy with 4-bit Floating-Point Quantization-Aware Training

ICML 2026oral

Large Reasoning Models (LRMs) achieve strong problem-solving through long chain-of-thought, but their deployment is constrained by the high cost of full-precision inference and growing KV cache footprints. Microscaled FP4 formats enable efficient FP4 deployment; however, fully quantizing weights, ac…

Cited by 1SourceScholar
2025

AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference

ACL 2025finding

As large language models (LLMs) grow in parameter size and context length, computation precision has been reduced from 16-bit to 4-bit to improve inference efficiency. However, this reduction causes accuracy degradation due to activation outliers. Rotation-based INT4 methods address this via matrix…

2025

LLM-guided Plan and Retrieval: A Strategic Alignment for Interpretable User Satisfaction Estimation in Dialogue

NAACL 2025long

Understanding user satisfaction with conversational systems, known as User Satisfaction Estimation (USE), is essential for assessing dialogue quality and enhancing user experiences. However, existing methods for USE face challenges due to limited understanding of underlying reasons for user dissatis…

Cited by 0SourcePDFScholar
2024

Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder

CVPR 2024poster

Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods however generate images only at fixed-scale magnification and suffer from over-smoothing and artifacts. Additionally they do not offer enough div…

Cited by 18SourcePDFScholar
2020

BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations

ICLR 2020poster

Binary Neural Networks (BNNs) have been garnering interest thanks to their compute cost reduction and memory savings. However, BNNs suffer from performance degradation mainly due to the gradient mismatch caused by binarizing activations. Previous works tried to address the gradient mismatch problem…

Cited by 55SourcecodeScholar
2020

Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks

NeurIPS 2020poster

For the gradient computation across the time domain in Spiking Neural Networks (SNNs) training, two different approaches have been independently studied. The first is to compute the gradients with respect to the change in spike activation (activation-based methods), and the second is to compute the…

2015

FBG-based polymer-molded shape sensor integrated with minimally invasive surgical robots

ICRA 2015poster

Shape tracking using a fiber Bragg grating sensor is a promising tool due to its thin, flexible, and weightless nature. Conventional investigations attached optical fibers with a metal rod which limited the curvature due to its stiffness and increased distance between the center of the fiber and tha…

Cited by 21SourceScholar