Wednesday, September 26, 2018

Machined Learnings: Sample Variance Penalization

Most of the time, supervised machine learning is done by optimizing the average loss on the training set, i.e. empirical risk minimization, perhaps with a (usually not data-dependent) regularization term...

Lincoln Laboratory enters licensing agreement to produce its localizing ground-penetrating radar

MIT has reached agreement with Geophysical Survey Systems, Inc. (GSSI) to develop commercial prototypes of a technology that helps autonomous vehicles navigate by using subsurface geology. Engineers at MIT Lincoln...

[D] How to deal with non-Markovian decision processes with large/infinite horizon using MCTS? :...

Quick google search will tell you that MCTS is applicable to large/infinite horizon RL tasks. But it seems that there's no empirical confirmation that it works as well as on...

Machined Learnings: NIPS 2015 Review

NIPS 2015 was bigger than ever, literally: at circa 3700 attendees this was roughly twice as many attendees as last year, which in turn was roughly twice as many as...

Artificial intelligence suggests recipes based on food photos

There are few things social media users love more than flooding their feeds with photos of food. Yet we seldom use these images for much more than a quick scroll...

[P] A Global Optimization Algorithm Worth Using : MachineLearning

This is really cool! Took me a minute to grok, so hopefully I can save someone a tiny bit of effort, and anyone who understands this better than me can...

Machined Learnings: Attention: Can we formalize it?

In statistics the bias-variance tradeoff is a core concept. Roughly speaking, bias is how well the best hypothesis in your hypothesis class would perform in reality, whereas variance is...

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