2021 Artificial Intelligence Diabetes Datathon

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Deep LearningAIDD2021
Overview

A I D D 2021 최종 포스터

A.I.D.D. 2021

2021 Artificial Intelligence Diabetes Datathon

A.I.D.D. 2021은 ‘2021 인공지능 학습용 데이터 구축사업’을 통해 만들어진 학습용 데이터를 활용하여 당뇨병을 효과적으로 예측할 수 있는가에 대한 AI 모델링 챌린지입니다.

본 대회는 NAVER CLOUD PLATFORM의 고성능 클라우드 인프라 상에서 운영되며 네이버의 클라우드 머신러닝 플랫폼인 NSML(Naver Smart Machine Learning)과 함께 합니다. NAVER CLOUD PLATFORMNSML은 개발자들이 "모델 개발과 알고리즘 최적화"에만 집중할 수 있도록 필요한 제반 환경을 제공합니다. AI 전문가들과 함께 인공지능 모델 개발에 도전하실 분들을 기다리고 있습니다.

챌린지

당뇨병 데이터를 이용하여 당뇨병 발생을 예측하는 인공지능 모델 개발

  1. 예선
  • 당뇨병 발생 예측 인공지능 모델 개발
  1. 본선
  • 당뇨병 발생 예측 인공지능 모델 고도화!

시상 및 혜택

  • 총상금: 추후 공개
구분 시상 상금
대상 (1팀)
경희의료원장상 500만원
최우수상 (1팀)
경희의과학연구원장상 300만원
우수상 (2팀)
인공지능빅데이터팀장상 100만원

대회 일정

행사내용 일정 장소/방식
참가 신청
2021년 10월 22일 ~ 11월 16일 온라인
개회식 및 설명회
2021년 11월 18일 14:00~ 온라인
예선 대회
2021년 11월 19일 ~ 11월 22일 온라인(NSML)
본선 대회
2021년 11월 26일 ~ 11월 29일 온라인(NSML)

심사기준

  • 서면평가: 참가신청서, 참가팀 역량 (예선 진출팀 40개 팀 선발)
  • 예선: NSML 리더보드 상위 점수 순으로 선발 (본선 진출 20개 팀 선발)
  • 본선: 종료 시점 NSML 리더보드 상위 점수 순으로 시상
    *모델 사이즈 제한 300MB
    *동점자 발생 시 모델 제출 시간이 빠른 순서, 모델 크기가 작은 순서 순으로 우선순위 결정

참가신청

  1. 신청 기간: 2021년 10월 22일 ~ 11월 16일
  2. 신청 방법: 온라인

Github 게시판

  • 온라인 게시판 대회기간 중 10:00~19:00 실시간 운영
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Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Music Source Separation with Channel-wise Subband Phase Aware ResUnet (CWS-PResUNet) Introduction This repo contains the pretrained Music Source Separ

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FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
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LaneDetectionAndLaneKeeping This project is part of my bachelor's thesis. The go

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Introduction to CPM

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Tsinghua AI 136 Dec 23, 2022
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BlockUnexpectedPackets This script automatically blocks DDoS attacks that are sp

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Neural Style Transfer (NST) Using TensorFlow Demo TensorFlow TensorFlow is an end-to-end open source platform for machine learning. It has a comprehen

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ShICA Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging Install Move into the ShICA directory cd ShICA

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Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

HaloNet - Pytorch Implementation of the Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones. This re

Phil Wang 189 Nov 22, 2022