전체 글380 [2026-1] 정재훈 - AnEmpirical Evaluation of Geeric Convolutional and Recurrent Networksfor Sequence Modeling 더보기https://arxiv.org/abs/1803.01271 An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingFor most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given aarxiv.org 0.BE.. 2026. 2. 7. [2026-1] 김지은 - Denoising Diffusion Implicit Models 본 글에서는 DDPM(NeurIPS 2020)의 Markovian diffusion 구조로 인해 reverse sampling이 순차적 과정을 요구하는 한계를 살펴보고, 이를 non-Markovian inference 구조로 일반화하여 빠른 deterministic sampling을 가능하게 한 DDIM(ICLR 2021)을 살펴본다. 1. IntroductionDDPM의 generative process는 forward diffusion을 거꾸로 따라가는 구조다. 따라서 1) sampling에 수천 번의 sequential iteration 필요하고 2) 병렬화가 어렵다. DDIM은 “Diffusion 모델의 샘플링을 더 빠르게 만들 수는 없을까?”의 문제 의식에서 출발한 논문이다. 이 논문은 DDPM.. 2026. 2. 7. [2026-1] 백승우 - Self-Improving Pretraining:using post-trained models to pretrain better models Self-Improving Pretraining: using post-trained models to pretrain better modelsEnsuring safety, factuality and overall quality in the generations of large language models is a critical challenge, especially as these models are increasingly deployed in real-world applications. The prevailing approach to addressing these issues involvearxiv.org 2026. 2. 4. [2026-1] 이루가 - High-Resolution Image Synthesis with Latent Diffusion Models 논문 링크: https://arxiv.org/abs/2112.10752 High-Resolution Image Synthesis with Latent Diffusion ModelsBy decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism tarxiv.org 확산모델(Diffusion)의 고성능은 유지하면서 계산비용.. 2026. 1. 31. 이전 1 2 3 4 ··· 95 다음
