machinelearning   50123

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Machine Learning on 2K of RAM
This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices—such as those based on the Arduino Uno board having an 8-bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2KB RAM, and 32KB read-only flash.
machinelearning  arduino  iot  internetofthings  constrained 
12 hours ago by dlkinney
Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data | PNAS
Winged insects perform remarkable aerial feats in uncertain, complex fluid environments. This ability is enabled by sensation of mechanical forces to inform rapid corrections in body orientation. Curiously, mechanoreceptor neurons do not faithfully report forces; instead, they are activated by specific time histories of forcing. We find that, far from being a bug, neural encoding by biological sensors is a feature that acts as built-in temporal filtering superbly matched to detect body rotation. Indeed, this encoding further enables surprisingly efficient detection using only a small handful of neurons at key locations. Nature suggests smart data as an alternative strategy to big data, and neural-inspired sensors establish a paradigm in hyperefficient sensing of complex systems.
biology  biomechanics  biomemesis  machinelearning 
yesterday by madamim
Will compression be machine learning’s killer app? • Pete Warden's blog
Warden used to be chief technology officer for a company called Jetpac, which used neural networks to do interesting stuff with Instagram photos; then Google bought it, and he's working on machine learning there:
<p>One of the other reasons I think ML is such a good fit for compression is how many interesting results we’ve had recently with natural language. If you squint, you can see captioning as a way of radically compressing an image. One of the projects I’ve long wanted to create is a camera that runs captioning at one frame per second, and then writes each one out as a series of lines in a log file. That would create a very simplistic story of what the camera sees over time, I think of it as a narrative sensor.

The reason I think of this as compression is that you can then apply a generative neural network to each caption to recreate images. The images won’t be literal matches to the inputs, but they should carry the same meaning. If you want results that are closer to the originals, you can also look at stylization, for example to create a line drawing of each scene. What these techniques have in common is that they identify parts of the input that are most important to us as people, and ignore the rest.

It’s not just images.

There’s a similar trend in the speech world. Voice recognition is improving rapidly, and so is the ability to synthesize speech. Recognition can be seen as the process of compressing audio into natural language text, and synthesis as the reverse. You could imagine being able to highly compress conversations down to transmitting written representations rather than audio. I can’t imagine a need to go that far, but it does seem likely that we’ll be able to achieve much better quality and lower bandwidth by exploiting our new understanding of the patterns in speech.</p>
machinelearning  compression 
yesterday by charlesarthur
Tf-idf Transformer - PHP-ML - Machine Learning library for PHP
Tf–idf, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.
lang:en  MachineLearning  InformationRetrievel  PHP 
yesterday by DASKAjA
PhotoPrism: Browse your life in pictures
Photo management software that might be a good basis for better photo management.
software  webservice  photography  javascript  machinelearning  golang 
yesterday by rjkroege

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