분류 전체보기264 [2025-1] 임재열- Mamba: Linear-Time Sequence Modeling with Selective State Spaces Mamba는 2024년 Albert Gu와 Tri Dao가 제안한 모델입니다. [Mamba]https://arxiv.org/abs/2312.00752 Mamba: Linear-Time Sequence Modeling with Selective State SpacesFoundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated conv.. 2025. 2. 1. [2025-1] 유경석 - Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference (LCM) https://arxiv.org/pdf/2310.04378 https://blog.outta.ai/171 [2025-1] Latent Consistency Models : Synthesizing High-Resolution ImagesWith Few-Step Inference논문 링크: 2310.04378 참고 논문 리뷰 블로그 링크: Latent Consistency Models : Synthesizing High-Resolution ImagesWith Few-Step Inference 논문 리뷰 :: LOEWEN Latent Consistency Models : Synthesizing High-Resolution ImagesWith Few-Stblog.outta.ai SummaryStable Dif.. 2025. 2. 1. [2025-1] 주서영 - SRDiff : Single image super-resolution with diffusion probabilistic models SRDiff SRDiff: Single Image Super-Resolution with Diffusion Probabilistic ModelsSingle image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones, which is an ill-posed problem because one LR image corresponds to multiple HR images. Recently, learning-based SISR methods have garxiv.orgNeurocomputing 2021611회 인용※ 참고SR3 Image Super-Resolut.. 2025. 2. 1. [2025-1] 전윤경- Roformer: Enhanced Transformer with Rotary Position Embedding IntroductionRoPE: 회전 행렬을 사용하여 절대적인 위치를 인코딩하고 self attention 공식에 명시적인 상대적 위치 의존성을 통합함.유연한 시퀀스 길이 지원상대적 거리가 증가함에 따라 토큰 간의 의존도 감소linear self-attention 메커니즘에서도 상대적 위치 인코딩을 적용할 수 있는 능력 갖춤Roformer: 회전 위치 임베딩(RoPE)을 적용한 Transformer 모델 -> 기존 방법보다 우수한 성능 Background and Related Workpreliminary$ S_{N}=\left\{w_{i} \right\}_{i=1}^{N}$ : N개 인풋 토큰의 시퀀스.$ E_{N}=\left\{x_{i} \right\}_{i=1}^{N}$ : PE 가 적용되지.. 2025. 1. 31. 이전 1 ··· 21 22 23 24 25 26 27 ··· 66 다음
