Goto

Collaborating Authors

One-Sided Unsupervised Domain Mapping

Neural Information Processing Systems

In unsupervised domain mapping, the learner is given two unmatched datasets $A$ and $B$. Recent approaches have shown that when learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$, convincing mappings are obtained. This is done by learning a mapping that maintains the distance between a pair of samples. Moreover, good mappings are obtained, even by maintaining the distance between different parts of the same sample before and after mapping. We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint.


AI and crowdsourcing fueling mapping innovation to meet smart city and mobility needs

#artificialintelligence

Google and Apple loom so large over the field of digital mapping that it's understandable why it may seem they represent the beginning and the end of this market. But the demands of a wide range of services such autonomous vehicles and smart cities are giving rise to a new generation of mapping competitors who are pushing the boundaries of innovation. The fundamental approach to mapping used by the two giants, mixing satellite imagery and fleets of cars roaming the streets, is becoming archaic and too slow to meet the fast-moving needs of businesses in areas like ecommerce, drones, and forms of mobility. These services often have very specific needs that require real-time updates and far richer data. To address these challenges, new mapping companies are turning to artificial intelligence and crowdsourcing, among other things, to deliver far more complex geodata.


Building Mapping Applications with QGIS - Programmer Books

#artificialintelligence

QGIS is one of the premier open source Geographical Information Systems. While developing Python geospatial applications can be challenging, QGIS simplifies the process by combining the necessary geoprocessing libraries with a sophisticated user interface, all of which can be directly controlled using Python code. Starting with an introduction to QGIS and how to use the built-in QGIS Python Console, we will teach you how to write Python code that makes use of the geospatial capabilities of QGIS. Building on this, you will ultimately learn how to create your own sophisticated standalone mapping applications built on top of QGIS. You will learn how to use the Python Console as a window into the QGIS programming environment, and then use that environment to create your own Python scripts and plugins to customize QGIS.


[R] One-Sided Unsupervised Domain Mapping • r/MachineLearning

@machinelearnbot

In unsupervised domain mapping, the learner is given two unmatched datasets A and B. The goal is to learn a mapping that translates a sample in A to the analog sample in B. Recently CycleGAN and DiscoGAN have shown that when learning simultaneously a mapping from A to B and the inverse mapping from B to A, convincing mappings are obtained. In this work, we present a method of learning a mapping from A to B without the inverse mapping. This is done by learning a mapping that maintains the distance between a pair of samples. Feel free to ask questions.


Mapping Irma, but not really…

@machinelearnbot

We're discussing data visualization nowadays in my course, and today's topic was supposed to be mapping. However late last night I realized I was going to run out of time and decided to table hands on mapping exercises till a bit later in the course (after we do some data manipulation as well, which I think will work better). That being said, talking about maps seemed timely, especially with Hurricane Irma developing. In addition to what's on the slide I told the students that they can assume the map is given, and they should only think about how the forecast lines would be drawn. Everyone came up with "we need latitude and longitude and time".