AI 및 Data Analysis/Paper11 [PaSCient] Learning multi-cellular representations of single-cell transcriptomics data enables characterization ofpatient-level disease states Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease stateshttps://neurips.cc/virtual/2024/102865 NeurIPS Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level dAbstract: Over the years, single-cell transcriptomics has emerged as a prominent tool for understand.. 2025. 4. 9. [Hierarchical MIL] Incorporating Hierarchical Information into Multiple Instance Learning for Patient Phenotype Prediction with scRNA-seq Data 논문Incorporating Hierarchical Information into Multiple Instance Learning for Patient Phenotype Prediction with scRNA-seq Datahttps://www.biorxiv.org/content/10.1101/2025.02.10.637389v1.full.pdf깃허브https://github.com/minhchaudo/hier-mil GitHub - minhchaudo/hier-milContribute to minhchaudo/hier-mil development by creating an account on GitHub.github.com정리 2025. 3. 22. [ScRAT] Phenotype prediction from single-cell RNA-seq data using attention-based neural networks 논문 https://academic.oup.com/bioinformatics/article/40/2/btae067/7613064정리 Attention 기반 신경망을 사용한 / 단일 세포 RNA-Seq 데이터의 / 표현형 예측Attention 기반으로 진행하는 것이 이 논문의 핵심!Attentino은 중요한 정보에 집중하는 과정!표현형(phenotypes) 예측하는 것은 중요함.기존의 Builk tissue samples는 전체 평균을 취해서 세포간의 이질성을 반영하지 못함.➡️ 그래서 Attention이라는, 서로 얼마나 중요한 관계를 가지는지 집중해보자! 2025. 3. 22. [ScRAT 흐름] Phenotype prediction from single-cell RNA-seq data using attention-based neural networks 논문 https://academic.oup.com/bioinformatics/article/40/2/btae067/7613064 발표용으로 정리2025.03.22 - [AI 및 Data Analysis/Paper] - [ScRAT 정리] Phenotype prediction from single-cell RNA-seq data using attention-based neural networks [ScRAT 정리] Phenotype prediction from single-cell RNA-seq data using attention-based neural networks논문 https://academic.oup.com/bioinformatics/article/40/2/btae067/7613064 정리 A.. 2025. 3. 20. Adding Conditional Control to Text-to-Image Diffusion Models (CVPR2023) (Controllable Image Generation) RAG란?Query(질문)이 들어오면 q(x)로 임베딩, 이후 검색, Top-K 선택한계점 : 검색 품질 제한, 단순 추론, 리소스 낭비Introduction답변 생성 시, 여러개가 도출.SETP 1 : Retrieve on Demand ; Retrieve가 필요 여부 결정(실시간) 후 검색하든/하지않든STEP 2 : Generate segment in parallel ; 각 segment마다 critiqueREFLECTION TOKEN : Retrieve(검색 필요한가?), IsREL(관련성 판정), IsSUP(신뢰도 판정), IsUSE(답변 유용성 판정)STEP 3MethodRetrieve on Demand는 segment를 만들 때, 실시간으로 적용장점검색 품질 향상자체 검증추론 능력 강화일관성 향.. 2025. 2. 1. Zero-Shot Text-to-Image Generation (DALL-E) https://arxiv.org/abs/2102.12092 Zero-Shot Text-to-Image GenerationText-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentatiarxiv.org 연구 목적기존의 작은 데이터셋에서 벗어나, 대규모 데이터 & 파라미터를 활용한 → 고품질 이미지 생성Backgroun.. 2025. 1. 31. 이전 1 2 다음
