분류 전체보기324 [2024-1] 김경훈 - VoteNet (Deep Hough Voting for 3D Object Detection in Point Clouds) https://arxiv.org/abs/1904.09664 Deep Hough Voting for 3D Object Detection in Point CloudsCurrent 3D object detection methods are heavily influenced by 2D detectors. In order to leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids (i.e., to voxel grids or to bird's eye view images), or rely on detection inarxiv.org VoteNet(CVPR, 2020) Indoor Scenes은 기본적으로 .. 2024. 5. 14. [2024-1] 김경훈 - Point Transformer https://arxiv.org/abs/2012.09164 Point TransformerSelf-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we investigate the application of self-attentionarxiv.org 1. Introduction 기존의 3D point cloud에 대한 접근 방식은 일반적으로 다음과 같습니다: Voxelize 3D space Sp.. 2024. 5. 14. [2024-1] 현시은 - Optimizing LLM Queries in Relational Workloads 원본 논문 링크 : https://arxiv.org/abs/2403.05821 Optimizing LLM Queries in Relational WorkloadsAnalytical database providers (e.g., Redshift, Databricks, BigQuery) have rapidly added support for invoking Large Language Models (LLMs) through native user-defined functions (UDFs) to help users perform natural language tasks, such as classification, entarxiv.org Abstract LLM은 굉장히 범용적으로 사용되고 있지만, LLM infe.. 2024. 5. 14. [2024-1] 박지연 - Rectified Linear Units(ReLU) Improve Restricted Boltzmann Machines https://www.cs.toronto.edu/~hinton/absps/reluICML.pdfRestricted Boltzmann Machine(RBM)보통 generative model이라고 하는데 ANN, DNN, CNN, RNN 등과 같은 deterministic model들과 다른 목표를 가짐 → deterministic model들은 타겟과 가설 간의 차이를 줄여서 오차를 줄이는 것이 목표 , generative model들의 목표는 확률밀도 함수를 모델링하는 것https://angeloyeo.github.io/2020/10/02/RBM.htmlRBM은 이렇듯 확률분포(정확하게는 pdf, pmf)를 학습하기 위해 만들어졌다고 할 수 있다RBM의 구조-> 많은 RBM의 연구에서 visible u.. 2024. 5. 12. 이전 1 ··· 63 64 65 66 67 68 69 ··· 81 다음
