MS MARCO(Microsoft Machine Reading Comprehension) is a large scale dataset focused on machine reading comprehension, question answering, and passage ranking. A variant of this task will be the part of TREC and AFIRM 2019. For Updates about TREC 2019 please follow This Repository Passage Reranking task Task Given a query q and a the 1000 most relevant passages P = p1, p2, p3,... p1000, as retrieved by BM25 a succeful system is expected to rerank the most relevant passage as high as possible. For this task not all 1000 relevant items have a human labeled relevant passage. Evaluation will be done using MRR
Обновлено 2023-06-12 21:21:58 +03:00
Deep Metric Transfer for Label Propagation with Limited Annotated Data
Обновлено 2023-06-03 07:09:23 +03:00
Data labels and scripts for fastMRI.org
Обновлено 2021-09-08 00:01:53 +03:00
Deep learning-based multi-label audio retrieval using a Siamese network
Обновлено 2019-09-20 03:40:45 +03:00
2019 Internship project: multilabel clustering for audio
Обновлено 2019-08-24 04:10:39 +03:00
Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
microsoft
azure-storage
image-classification
neural-networks
cntk
microsoft-azure
land-cover
land-use
geospatial-data
image-segmentation
microsoft-machine-learning
azure-batchai
cntk-model
geospatial-analysis
Обновлено 2019-07-25 06:53:28 +03:00