8 research outputs found
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Predicting when and where events will occur in cities, like taxi pick-ups,
crimes, and vehicle collisions, is a challenging and important problem with
many applications in fields such as urban planning, transportation optimization
and location-based marketing. Though many point processes have been proposed to
model events in a continuous spatio-temporal space, none of them allow for the
consideration of the rich contextual factors that affect event occurrence, such
as weather, social activities, geographical characteristics, and traffic. In
this paper, we propose \textsf{DMPP} (Deep Mixture Point Processes), a point
process model for predicting spatio-temporal events with the use of rich
contextual information; a key advance is its incorporation of the heterogeneous
and high-dimensional context available in image and text data. Specifically, we
design the intensity of our point process model as a mixture of kernels, where
the mixture weights are modeled by a deep neural network. This formulation
allows us to automatically learn the complex nonlinear effects of the
contextual factors on event occurrence. At the same time, this formulation
makes analytical integration over the intensity, which is required for point
process estimation, tractable. We use real-world data sets from different
domains to demonstrate that DMPP has better predictive performance than
existing methods.Comment: KDD 1
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
The convolutional neural network (CNN), which is one of the deep learning
models, has seen much success in a variety of computer vision tasks. However,
designing CNN architectures still requires expert knowledge and a lot of trial
and error. In this paper, we attempt to automatically construct CNN
architectures for an image classification task based on Cartesian genetic
programming (CGP). In our method, we adopt highly functional modules, such as
convolutional blocks and tensor concatenation, as the node functions in CGP.
The CNN structure and connectivity represented by the CGP encoding method are
optimized to maximize the validation accuracy. To evaluate the proposed method,
we constructed a CNN architecture for the image classification task with the
CIFAR-10 dataset. The experimental result shows that the proposed method can be
used to automatically find the competitive CNN architecture compared with
state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our
method is available at https://github.com/sg-nm/cgp-cn