A major research project has been to make these historical documents accessible and understandable.
From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subject matter.
We address temporal localization of events in large-scale video data, in the context of the Youtube-8M Segments dataset.
Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks.
Ranked #4 on Image Classification on Kuzushiji-MNIST (Error metric)
This work addresses the problem of accurate semantic labelling of short videos.
We investigate factors controlling DNN diversity in the context of the Google Cloud and YouTube-8M Video Understanding Challenge.