Author

Jae-Hwan Kim

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Open access 2026

Hybrid Spiking LoRA: Asymmetric Bit-Width Design for Language Model Adaptation With Theoretical Neuromorphic Efficiency Potential—A Case Study on Korean NLU

This paper proposes Hybrid Spiking LoRA, an asymmetric spiking adapter that replaces the LoRA down-projection with a one-bit spiking encoder while retaining a full-precision up-projection, and evaluates the method on Korean natural language understanding tasks covering topic classification, relation extraction, natural language inference, and extractive question answering.

Jae-Hwan Kim, Dae-Yeol Kim, Chae-Bong Sohn · IEEE Access · 0 citations

Preprint Aug 2026

TESLA: Taylor Expansion of Sinusoidal Learnable Activations

TESLA, an activation defined as a learnable combination of sine and cosine terms, enabling explicit control over polynomial degree and selective amplification of high-order components is proposed, indicating that activation-level degree control transfers to more general vision workloads.

Daehwa Ko, Jae-Hwan Kim, Seunghyun Ham et al. · 0 citations