Attention and Reinforcement in Learning — summary of a recent article available on Sci-Hub (no preprints available on the authors’ sites). The results also showed that selective attention shapes what we learn when something unexpected happens. For example, if your pizza is better or worse than expected, you attribute the learning to whatever your attention was focused on and not to features you decided to ignore. Finally, the researchers found that what we learn through this process teaches us what to pay attention to, creating a feedback cycle — we learn about what we attend to, and we attend to what we learned high values for. See also Reprioritizing Attention in Fast Data.
First Batch of Reproducibility Project Results Are In — Elizabeth Iorns and others at the Reproducibility Project tried to reproduce landmark findings in cancer research, with only some success thus far. [P]erhaps the most important result from the project so far, as Daniel Engber wrote in Slate, is that it has been “a hopeless slog.” “If people had deposited raw data and full protocols at the time of publication, we wouldn’t have to go back to the original authors,” says Iorns. That would make it much easier for scientists to truly check each other’s work. The National Institutes of Health seem to agree. In recently released guidelines, meant to improve the reproducibility of research, they recommend that journals ask for more thorough methods sections and more sharing of data. And in this, the Reproducibility Project have modeled the change they want to see, documenting every step of their project on a wiki.
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Deep Traffic — a road simulator where you must code the self-driving car neural network, which is part of Deep Learning for Self-Driving Cars at MIT. If you are officially registered for this class you need to perform better than 65 mph to get credit for this assignment.
rawgraphs.io — an open source data visualization framework built with the goal of making the visual representation of complex data easy for everyone.