I was frustrated with how difficult I found making animations in matplotlib so I wrote something to make it easy and called it celluloid. I found the idea in plotnine and simply took out the plotnine specific code and generalized it some more (adding support for subplots).
The goal is that your visualization code shouldn't need to modified at all or as little as possible. With celluloid you take "photos" of your visualization to create each frame. Once all the frames have been captured you can create an animation with one call. The readme has more details.
Spatial co-location pattern mining refers to the task of discovering the group of objects or events that co-occur at many places. Extracting these patterns from spatial data is very difficult due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation. We model the co-location pattern discovery as a clique enumeration problem over a neighborhood graph (which is materialized using a distributed graph database). Further, we propose three new traversal based algorithms, namely CliqueEnumG, CliqueEnumK and CliqueExtend. These algorithms allow for a trade-off between time and memory requirements and support interactive data analysis without having to recompute all the intermediate results.
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Hi there -- one of the authors of the paper here. Optimal stopping problems constitute an important class of stochastic control problems, with many real applications, such as pricing financial options. Typically they are solved using approximate dynamic programming methods, which involve coming up with some approximation of the value function or the continuation value function.
In this paper, we take a different approach, where we represent the stopping policy as a tree, and propose a methodology for learning this tree from the data; so in the same way that one comes up with a tree for predicting a binary label in classification, or predicting a continuous value in regression, one obtains a tree that prescribes an action for each possible state. We show using a standard benchmark problem in option pricing that these tree policies perform very well, while being as simple and interpretable as tree models used in other areas of machine learning. We appreciate any questions or comments!
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We were at ECCV 2 weeks ago. This is a recap on two verticals (we voluntarily did not made the recap exhaustive)
- new nn architectures (mostly for detection and segmentation purposes)
- 6D pose estimation using RGB (not RGB-D) images and the 3D model.
Hope you find this interesting.