人工智能/数据科学比赛汇总 2019.3

内容来自
DataSciComp,人工智能/数据科学比赛整理平台。

Github:iphysresearch/DataSciComp

本项目由 ApacheCN 强力支持。

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全球城市计算AI挑战赛

3月19日 – 4月11日, 2019 // Host by 天池 // Prize: ¥300000

Note: 大赛以“地铁乘客流量预测”为赛题,参赛者可通过分析地铁站的历史刷卡数据,预测站点未来的客流量变化,帮助实现更合理的出行路线选择,规避交通堵塞,提前部署站点安保措施等,最终实现用大数据和人工智能等技术助力未来城市安全出行。

Histopathologic Cancer Detection

Novenber 16, 2018 – March 30, 2019 // Host by Kaggle // Prize: Kaggle Swag

Note: Identify metastatic tissue in histopathologic scans of lymph node sections

Building Educational Applications 2019 Shared Task: Grammatical Error Correction

Jan 25 – August 2, 2019 // Host by CodaLab & BEA 2019 Shared Task Discussion Group // Prize: NaN

Note: Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in text; e.g. [I follows his advices -> I followed his advice].
There are 3 tracks in the BEA 2019 shared task:
Restricted Track
Unrestricted Track
Low Resource Track

Histopathologic Cancer Detection

Now – March 30, 2019 // Host by Kaggle & Grand Challenges // Prize: NaN

Note: Identify metastatic tissue in histopathologic scans of lymph node sections

SegTHOR: Segmentation of THoracic Organs at Risk in CT images

Jan. 5, 2018 – Apr. 8, 2019 // Host by CodaLab & Grand Challenges & ISBI 2019// Prize: NaN

Note: Our challenge addresses the problem of organs at risk segmentation in Computed Tomography (CT) images.

CheXpert: A Large Chest X-Ray Dataset And Competition

Jan. 5, 2018 – Apr. 8, 2019 // Host by Stanford ML Group & Grand Challenges // Prize: NaN

Note: CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets. Develop an algorithm to determine the presence of 14 different abnormalities given a chest radiograph.

MolHack 2019

Feb. 24 – Mar. 31 2019 // Host by CodaLab & CVPR 2019 // Prize: NaN

Note: Verify your residency of Taiwan!
At the minimum, the MolHack challenge will require the participants to build and train a Conditional Generative DNN model that can generate new small molecules such that the newly generated small molecules have similar MACCS fingerprints as the target MACCS fingerprint upon which the generation of the molecules is conditioned.

Gridlock Prediction Challenge

March 1-31, 2019 // Host by 天池 // Prize: iPhone/iPad/AirPods

Note: Task: To estimate the average travel time from designated intersections to tollgates

ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction

February 10 – April 30 September 2019 // Host by ICDAR 2019 & Baidu // Prize: NaN

Note: Scanned receipts OCR and information extraction (SROIE) play critical roles in streamlining document-intensive processes and office automation in many financial, accounting and taxation areas.

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    原文作者:人工智能
    原文地址: https://segmentfault.com/a/1190000018713417
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