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Nemanja Djuric

Researcher at Uber

Publications -  89
Citations -  4114

Nemanja Djuric is an academic researcher from Uber . The author has contributed to research in topics: Web search query & Context (language use). The author has an hindex of 25, co-authored 88 publications receiving 3255 citations. Previous affiliations of Nemanja Djuric include Yahoo! & Temple University.

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Journal ArticleDOI

A large-scale evaluation of computational protein function prediction

Predrag Radivojac, +107 more
- 01 Mar 2013 - 
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
Proceedings ArticleDOI

Hate Speech Detection with Comment Embeddings

TL;DR: This work proposes to learn distributed low-dimensional representations of comments using recently proposed neural language models, that can then be fed as inputs to a classification algorithm, resulting in highly efficient and effective hate speech detectors.
Proceedings ArticleDOI

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

TL;DR: This work presents a method to predict multiple possible trajectories of actors while also estimating their probabilities, and successfully tested on SDVs in closed-course tests.
Proceedings ArticleDOI

E-commerce in Your Inbox: Product Recommendations at Scale

TL;DR: In this article, a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users is described, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence.
Proceedings ArticleDOI

Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

TL;DR: A deep learning-based approach that takes into account a current world state and produces raster images of each actor’s vicinity that is used to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task.