Search Results for author: Vladan Radosavljevic

Found 13 papers, 3 papers with code

Multi-Modal Trajectory Prediction of NBA Players

no code implementations18 Aug 2020 Sandro Hauri, Nemanja Djuric, Vladan Radosavljevic, Slobodan Vucetic

National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game.

Decision Making Trajectory Prediction

Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

1 code implementation20 Jun 2019 Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui, Vladan Radosavljevic, Thi Nguyen, Tzu-Kuo Huang, Matthew Niedoba, Jeff Schneider, Nemanja Djuric

Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment.

motion prediction

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

3 code implementations18 Sep 2018 Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, Nemanja Djuric

Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact.

Autonomous Driving Motion Planning +1

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

no code implementations17 Aug 2018 Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Nitin Singh, Jeff Schneider

We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings.

Autonomous Driving motion prediction

Proceedings of the 2017 AdKDD & TargetAd Workshop

no code implementations11 Jul 2017 Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee, Kun Liu, Vladan Radosavljevic, Suju Rajan

Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax, Nova Scotia, Canada.

Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

no code implementations7 Jul 2016 Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza-Yates, Andrew Feng, Erik Ordentlich, Lee Yang, Gavin Owens

For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on.

Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests

no code implementations29 Jun 2016 Nemanja Djuric, Mihajlo Grbovic, Vladan Radosavljevic, Narayan Bhamidipati, Slobodan Vucetic

We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertisers' revenue.

Retrieval

Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

no code implementations28 Jun 2016 Nemanja Djuric, Hao Wu, Vladan Radosavljevic, Mihajlo Grbovic, Narayan Bhamidipati

In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them.

Network-Efficient Distributed Word2vec Training System for Large Vocabularies

no code implementations27 Jun 2016 Erik Ordentlich, Lee Yang, Andy Feng, Peter Cnudde, Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Gavin Owens

Word2vec is a popular family of algorithms for unsupervised training of dense vector representations of words on large text corpuses.

Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior

no code implementations15 Dec 2015 Farshad Kooti, Kristina Lerman, Luca Maria Aiello, Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic

Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases.

Social and Information Networks Computers and Society

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