59 citations · 83 across the 10 of their papers we have counts for
5 papers · 1 filter
LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
Jinwoo Ahn, Ingyu Seong, Akhil Kedia +4
Transformer-based large language models (LLMs) rely on key-value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves…
On the Importance of a Multi-Scale Calibration for Quantization
Seungwoo Son, Ingyu Seong, Junhan Kim +2
Post-training quantization (PTQ) is a cornerstone for efficiently deploying large language models (LLMs), where a small calibration set critically affects quantization performance.…
DAFA: Distance-Aware Fair Adversarial Training
Hyungyu Lee, Saehyung Lee, Hyemi Jang +3
The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem. Existing methodologies aim…
Stein Latent Optimization for Generative Adversarial Networks
Uiwon Hwang, Heeseung Kim, Dahuin Jung +3
Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attribute…
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
Uiwon Hwang, Jaewoo Park, Hyemi Jang +2
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise the network at the inferen…