11 citations · 15 across the 11 of their papers we have counts for
11 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…
Beyond Confidence: Test-Time Scaling for Multi-Turn Search Agents via Retrieval Grounding
Hyunho Kook, Junhyuk So, Tianyu Fu +2
Confidence-based voting aggregates parallel LLM rollouts by weighting each with internal signals such as token log probabilities, and has been actively studied for single-turn reas…
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…
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.…
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…
Temporal Dynamic Quantization for Diffusion Models
Junhyuk So, Jungwon Lee, Daehyun Ahn +2
The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility. However, high storage and computation demands, result…
Learning Fair Representation via Distributional Contrastive Disentanglement
Changdae Oh, Heeji Won, Junhyuk So +4
Learning fair representation is crucial for achieving fairness or debiasing sensitive information. Most existing works rely on adversarial representation learning to inject some in…
NIPQ: Noise proxy-based Integrated Pseudo-Quantization
Juncheol Shin, Junhyuk So, Sein Park +3
Straight-through estimator (STE), which enables the gradient flow over the non-differentiable function via approximation, has been favored in studies related to quantization-aware…
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Changdae Oh, Junhyuk So, Hoyoon Byun +4
Pre-trained multi-modal models, such as CLIP, provide transferable embeddings and show promising results in diverse applications. However, the analysis of learned multi-modal embed…