eref

6 ms·

1.
by eref 9y ago
I do not get what makes this post so popular. Google Maps is often better and uses automatically extracted features from photogrammetry. Eh. 350 points at most.
2.
by eref 9y ago
For me on Firefox on macOS that is just a couple of key strokes away: cmd-l cmd-c ctrl-a r <space> u r l : <enter> cmd-t cmd-v ctrl-a h n <enter> I have set up `r` as keyword to search on Reddit and `hn` to search o
3.
The Elephant in the Brain – A New Book by Kevin Simler and Robin Hanson(elephantinthebrain.com)
3 points by eref 9y ago|0 comments
4.
by eref 9y ago
Why not both?
5.
2 Navy Airmen and an Object That ‘Accelerated Like Nothing I’ve Ever Seen’(nytimes.com)
35 points by eref 9y ago|34 comments
6.
by eref 9y ago
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by eref 9y ago
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8.
by eref 9y ago
The term "circuit" is used in neuroscience all of the place.
9.
by eref 9y ago
Capsules basically do a kind of self-attention. But there the parent features compete for a coupling, not the child features.
10.
by eref 9y ago
Same with 57.0 on macOS 10.12. Edit: I have FF Studies disabled under about:preferences#privacy. I guess that is the reason why it is not installed on my machine.
11.
by eref 9y ago
The new XPS 13 has a better camera position and a good touch pad but still not a great battery. It’s close. Maybe next year.
12.
by eref 9y ago
> simulate a physical environment very fast That's probably only a problem if it is must faster than everbody else. > let alone faster than what happens in our environment That is often not very hard. When a bottle rolls off the
13.
Learning Combinatorial Optimization Algorithms Over Graphs(arxiv.org)
88 points by eref 9y ago|1 comments
14.
by eref 9y ago
The question is whether the overall gains outnumber the local losses.
15.
by eref 9y ago
The main problem is that we still lack good generative models and good ways of interrogating them. GANs are unstable and difficult to apply to time series, VAEs suffer from posterior collapse, WaveNet/PixelRNN grow with the input size
16.
by eref 9y ago
But what does have to do with smoothness and translation invariance which this paper is a demonstration of? You even learn Gabor filters with local connectivity without spatial weight sharing.
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Russia says it will ignore any UN ban of killer robots(businessinsider.com)
4 points by eref 9y ago|0 comments
18.
by eref 9y ago
What do Gabor filters have to do with this?
19.
by eref 9y ago
I think that can be mainly attributed to the fact that the last few deconvolutional features are overfitted to features in the image and are somewhat robust to noise. The network does not even learn features to produce e.g. white noise as
20.
by eref 9y ago
I think it is right to „soft“-censor in this way (i.e. the information is accessible but not promoted). Reality without sorting and filters would be inhuman because market incentives to grab people’s attention would maximally exploit our tr
21.
by eref 9y ago
Maybe I misunderstand stoic philosophy, but the rule to override emotional responses to failure with serenity strikes me as overly broad. Sometimes it is simply easier to get angry and let it out; especially if that sends a valid corrective
22.
by eref 9y ago
> High representation efficiency Capsules do require much fewer parameters, they generalize 10-20% better to new viewpoints, they are much more robust to adversarial examples and can better recognize overlapping objects; but, on the othe
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by eref 9y ago
I might be wrong, and I don't want to question the possibility that someone from a non-technical domain can contribute to technical domains (in particular because neural networks and style transfer are basically high school mathematics
24.
by eref 9y ago
I think that is correct because otherwise they would have mentioned it as an outstanding feature of the model. It does require fewer parameters than a CNN to reach the same accuracy.
25.
by eref 9y ago
The upgrade to 10.13.1 has effectively locked my SSD in read-only mode because reading from it would always cause an APFS kernel panic (both under 10.13 and 10.13.1) also making it impossible to boot from it. It took me several hours to fig
26.
by eref 9y ago
Skimming the two papers I could not find any figure about data efficiency. Did you?
27.
by eref 9y ago
> I was thinking that the next layer in the network would respond to multiple samples (i.e. convolutions of the Gaussian at different positions) and, as long as you didn't have too many active neurons on the previous layer, it could
28.
by eref 9y ago
You'd also lose most of the information. If there is only a single active neuron among the inputs to a Gaussian kernel neuron, you would at least have info about the distance of that to the center of the receptive field, but no directi
29.
by eref 9y ago
The same problem occurs with avg pooling. Strided conv also allows to "pool" neurons in the layer below to reduce the number of neurons in subsequent layers, but, in practice, deeper neurons then also have trouble learning precise
30.
by eref 9y ago
You can maybe find a alternatives here: https://docs.google.com/spreadsheets/u/1/d/1TFcEXMcKrwoIAECI...

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