AI/Paper - Theory (70) 썸네일형 리스트형 [Mamba 논문 리뷰 4] - Mamba: Linear-Time Sequence Modeling with Selective State Spaces *Mamba 논문 리뷰 시리즈4 입니다! 궁금하신 점은 댓글로 남겨주세요!시리즈 1: Hippo시리즈 2: LSSL시리즈 3: S4시리즈 4: Mamba시리즈 5: Vision MambaMamba paper: 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 sub.. [Mamba 논문 리뷰 3] - S4: Efficiently Modeling Long Sequences with Structured State Spaces *Mamba 논문 리뷰 시리즈3 입니다! 궁금하신 점은 댓글로 남겨주세요!시리즈 1: Hippo시리즈 2: LSSL시리즈 3: S4시리즈 4: Mamba시리즈 5: Vision MambaS4 paper: [2111.00396] Efficiently Modeling Long Sequences with Structured State Spaces (arxiv.org) Efficiently Modeling Long Sequences with Structured State SpacesA central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modal.. [Mamba 논문 리뷰 2] - LSSL: Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers *Mamba 논문 리뷰 시리즈2 입니다! 궁금하신 점은 댓글로 남겨주세요!시리즈 1: Hippo시리즈 2: LSSL시리즈 3: S4시리즈 4: Mamba시리즈 5: Vision MambaLSSL paper: [2110.13985] Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers (arxiv.org) Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space LayersRecurrent neural networks (RNNs), temporal convolutions, and neural d.. [Mamba 논문 리뷰 1] - HiPPO: Recurrent Memory with Optimal Polynomial Projections *Mamba 논문 리뷰 시리즈1 입니다! 궁금하신 점은 댓글로 남겨주세요!시리즈 1: Hippo시리즈 2: LSSL시리즈 3: S4시리즈 4: Mamba시리즈 5: Vision MambaHiPPO paper: https://arxiv.org/abs/2008.07669 HiPPO: Recurrent Memory with Optimal Polynomial ProjectionsA central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO) for the o.. [LGM 논문 리뷰] Large Multi-View Gaussian Model for High-Resolution 3D Content Creation *LGM를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LGM github: LGM (kiui.moe) LGMLGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation Arxiv 2024 Jiaxiang Tang1, Zhaoxi Chen2, Xiaokang Chen1, Tengfei Wang3, Gang Zeng1, Ziwei Liu2 1 Peking University 2 S-Lab, Nanyang Technological University 3 Shanghai AI Lame.kiui.moeContents1. Simple Introduction2. Background Knowledge: Gaussia.. [3D Gaussian Splatting 간단한 논문 리뷰] *Gaussian Splatting에 대한 간단한 논문 리뷰 입니다!*이해를 돕기 위해 수식은 거의 제외했습니다. GS 논문: repo-sam.inria.fr/fungraph/3d-gaussian-splatting/3d_gaussian_splatting_high.pdf GS github: 3D Gaussian Splatting for Real-Time Radiance Field Rendering (inria.fr) 3D Gaussian Splatting for Real-Time Radiance Field Rendering[Müller 2022] Müller, T., Evans, A., Schied, C. and Keller, A., 2022. Instant neural graphics primitives.. [LRM 논문 리뷰] - LARGE RECONSTRUCTION MODEL FOR SINGLE IMAGE TO 3D *LRM를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LRM paper: https://arxiv.org/abs/2311.04400 LRM: Large Reconstruction Model for Single Image to 3DWe propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specarxi.. [FNO 논문 리뷰 & 코드 리뷰] - FOURIER NEURAL OPERATOR FOR PARAMETRIC PARTIAL DIFFERENTIAL EQUATIONS *FNO를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! FNO paper: [2010.08895] Fourier Neural Operator for Parametric Partial Differential Equations (arxiv.org) Fourier Neural Operator for Parametric Partial Differential EquationsThe classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural oper.. [Diffusion Transformer 논문 리뷰3] - Scalable Diffusion Models with Transformers *DiT를 한번에 이해할 수 있는(?) A~Z 논문리뷰입니다! *총 3편으로 구성되었고, 마지막 3편은 제 온 힘을 다하여서.. 논문리뷰를 했습니다..ㅎㅎ *궁금하신 점은 댓글로 남겨주세요! DiT paper: https://arxiv.org/abs/2212.09748 Scalable Diffusion Models with Transformers We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates o.. [Diffusion Transformer 논문 리뷰2] - High-Resolution Image Synthesis with Latent Diffusion Models *DiT를 한번에 이해할 수 있는(?) A~Z 논문리뷰입니다! *총 3편으로 구성되었고, 2편은 DiT를 이해하기 위하여 LDM를 논문리뷰를 진행합니다! *궁금하신 점은 댓글로 남겨주세요! DiT paper: https://arxiv.org/abs/2212.09748 Scalable Diffusion Models with Transformers We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on.. [Diffusion Transformer 논문 리뷰1] - DDPM, Classifier guidance and Classifier-Free guidance *DiT를 한번에 이해할 수 있는(?) A~Z 논문리뷰입니다! *총 3편으로 구성되었고, 1편은 DiT를 이해하기 위한 지식들을 Preview하는 시간입니다! *궁금하신 점은 댓글로 남겨주세요! DiT paper: https://arxiv.org/abs/2212.09748 Scalable Diffusion Models with Transformers We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates.. [SORA 설명] - OpenAI의 Video Generation AI (기술부분 번역 + 설명 이미지 추가) Technical Report: Video generation models as world simulators (openai.com) Video generation models as world simulators We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that oper openai.com SORA: https.. 이전 1 2 3 4 5 6 다음
