Facebook helped develop the Open Neural Network Exchange (ONNX) format to allow AI engineers to more easily move models between frameworks without having to do resource-intensive custom engineering. Today, we’re sharing that ONNX is adding support for additional AI tools, including Apple Core ML converter technology, Baidu’s PaddlePaddle platform, and Qualcomm SNPE.
Source: facebook.com
The paper attempts to predict whether crimes are gang-related using a neural network, a complex computational system modeled after a human brain that “learns” to classify or identify items based on ingesting a training dataset. The authors selected what they determined to be the four most important features (number of suspects, primary weapon used, the type of premises where the crime took place, and the narrative description of the crime) for identifying a gang-related crime from 2014–16 LAPD data and cross-referenced the crime incidents with a 2009 LAPD map of gang territory to create a training dataset for their neural network.
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In our paper, Measuring the Intrinsic Dimension of Objective Landscapes, to be presented at ICLR 2018, we contribute to this ongoing effort by developing a simple way of measuring a fundamental network property known as intrinsic dimension. In the paper, we develop intrinsic dimension as a quantification of the complexity of a model in a manner decoupled from its raw parameter count, and we provide a simple way of measuring this dimension using random projections. We find that many problems have smaller intrinsic dimension than one might suspect.
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