Dear all,
we will have a tea talk by Alexandre de Brébisson, Étienne Simon and Alex Auvolat this Wednesday, 26th August at 1:30pm in AA 3195. They will talk about their successful participation (1st place!) in the Kaggle Taxi Trajectory Prediction challenge:
Artificial Neural Networks Applied to Taxi Destination Prediction
We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the departure time, the driver id and client information. Contrary to most published competitor approaches, we used an almost fully automated approach based on neural networks and we ranked first out of 381 teams. The architectures we tried use multi-layer perceptrons, bidirectional recurrent neural networks and models inspired from recently introduced memory networks. Our approach could easily be adapted to other applications in which the goal is to predict a fixed-length output from a variable-length sequence.
Link to the paper: http://arxiv.org/abs/1508.00021
Hope to see you all there!
j
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Congratulations, guys! Another cool application of deep learning! I wish I was in town for the talk.
On Tue, Aug 25, 2015 at 11:51 AM, Jörg Bornschein bornj@iro.umontreal.ca wrote:
Dear all,
we will have a tea talk by Alexandre de Brébisson, Étienne Simon and Alex Auvolat this Wednesday, 26th August at 1:30pm in AA 3195. They will talk about their successful participation (1st place!) in the Kaggle Taxi Trajectory Prediction challenge:
Artificial Neural Networks Applied to Taxi Destination Prediction
We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the departure time, the driver id and client information. Contrary to most published competitor approaches, we used an almost fully automated approach based on neural networks and we ranked first out of 381 teams. The architectures we tried use multi-layer perceptrons, bidirectional recurrent neural networks and models inspired from recently introduced memory networks. Our approach could easily be adapted to other applications in which the goal is to predict a fixed-length output from a variable-length sequence.
Link to the paper: http://arxiv.org/abs/1508.00021
Hope to see you all there!
j
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