EMNLP 2019 was in Hong Kong and received a record-breaking number of submissions, but here is a very biased selection of what we liked from the things we saw.
Evaluating our method on public datasets, we show that it can successfully solve challenging situations in dynamic environments which cause state-of-the-art baseline VSLAM algorithms to fail and that it maintains performance on static scenes.
A combination of collaborative filtering approaches and contextual information can help overcome unpredictable behavior while taking into account position and layout bias for more effective recommendation.
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