1 citations · 1 across the 9 of their papers we have counts for
10 papers
LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models
Byeongho Yu, Junhyuk So, Eunhyeok Park
Looped Language Models (LoopLMs) perform "latent reasoning" by recursively refining internal latent representations with shared weights, offering a more effective alternative to ex…
Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradient
Hyunho Kook, Byeongho Yu, Jeong Min Oh +1
Recent advancements in the direct training of Spiking Neural Networks (SNNs) have demonstrated high-quality outputs even at early timesteps, paving the way for novel energy-efficie…
Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Junhyuk So, Chiwoong Lee, Shinyoung Lee +2
Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies wi…
Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation
Junhyuk So, Hyunho Kook, Chaeyeon Jang +1
Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrained by the slow inference speed o…
Grouped Speculative Decoding for Autoregressive Image Generation
Junhyuk So, Juncheol Shin, Hyunho Kook +1
Recently, autoregressive (AR) image models have demonstrated remarkable generative capabilities, positioning themselves as a compelling alternative to diffusion models. However, th…
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
MoonJeong Park, Sunghyun Choi, Jaeseung Heo +2
Oversmoothing has been recognized as a main obstacle to building deep Graph Neural Networks (GNNs), limiting the performance. This position paper argues that the influence of overs…
PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models
Junhyuk So, Jiwoong Shin, Chaeyeon Jang +1
Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes.…
FSPGD: Rethinking Black-box Attacks on Semantic Segmentation
Eun-Sol Park, MiSo Park, Yong-Goo Shin
Black-box adversarial attacks on semantic segmentation remain a challenging problem, particularly in the black-box transfer attack setting where perturbations crafted on a surrogat…
Diffusion Model Compression for Image-to-Image Translation
Geonung Kim, Beomsu Kim, Eunhyeok Park +1
As recent advances in large-scale Text-to-Image (T2I) diffusion models have yielded remarkable high-quality image generation, diverse downstream Image-to-Image (I2I) applications h…
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models
Junhyuk So, Jungwon Lee, Eunhyeok Park
The substantial computational costs of diffusion models, especially due to the repeated denoising steps necessary for high-quality image generation, present a major obstacle to the…