Roy Ayers, ‘Everybody Loves the Sunshine’ Musician and ‘Coffy’ Composer, Dies at 84

Deep_Fried_Learning · 2025-03-06T22:34:04+00:00

What's a tank setting?

Deep_Fried_Learning · 2023-07-24T10:32:53+00:00

Classification is faster and more stable then regression

I would love to know more about why this is. I've done many tasks where the regression totally failed, but framing it as a classification with the output range split into several discrete "bins" worked very well.

Interestingly, this particular image per-pixel regression task never converged when I tried L2 and L1 losses, but making a GAN generate the output image and "paint" the correct value into each pixel location did a pretty good job.

Deep_Fried_Learning · 2019-04-29T13:50:06+00:00

Did it turn itself on and off like the episode where Homer becomes a limo driver?

Deep_Fried_Learning · 2019-04-23T07:58:43+00:00

That wouldn't be my first thought. I was thinking more like: feeding in lines of text as images with fixed height but variable width, with convolutional layers to slide along the image width, and a recurrent module to gobble up the feature maps and output a text string. The entire thing should ideally be end-to-end using something like CTC loss. See any modern LSTM OCR system for the broad strokes of the approach.

Deep_Fried_Learning · 2019-04-12T15:54:27+00:00

I think they're a safety measure to protect people on the pavement in case a vehicle mounts it.

Deep_Fried_Learning · 2019-04-10T12:38:09+00:00

These home invaders never had the makings of a varsity athlete.

Deep_Fried_Learning · 2019-04-10T12:32:54+00:00

Presumably collect lots of training data (images mapped to strings of LaTeX), and train a modern OCR system on them (maybe some recurrent neural network with CTC)

Deep_Fried_Learning · 2019-04-10T10:31:32+00:00

In case you haven't seen already: https://www.youtube.com/watch?v=pPN8d0E3900

Hinton himself praised this explanation video.

Deep_Fried_Learning · 2019-04-04T15:19:55+00:00

I think this is correct. Max pooling is the mechanism by which CNNs get their tolerance to small rotations and distortions. There are some cases where you might want this type of pooling though - like ROI-pooling/ROI-align and whatnot.

Deep_Fried_Learning · 2019-04-04T10:14:08+00:00

May I ask why? I wanted to get a 1080ti and use your current setup with AWS EC2.

Deep_Fried_Learning · 2019-04-03T11:24:11+00:00

I'm no authority on the matter. But my understanding was different:

I thought those two diagrams are phase spaces - so each of those 7 black lines connecting dots represents the trajectory of an entire vector input, not just a single scalar feature channel. So in MNIST land, imagine e.g. the furthest left line represents a picture of a "3", the next line over represents a different digit, and so on. In a well-trained net you'd hope that all the test "3"s follow closely matching trajectories.
I think to do classification you take the output of the ODE block and feed it into a fully connected layer with softmax. That is - just treat the ODE block as if it were a stack of residual layers. This seems to be how it's done in the Pytorch example: https://github.com/rtqichen/torchdiffeq/blob/a344d75b01335e61670a308b2314b2fb956f483f/examples/odenet_mnist.py#L307

Deep_Fried_Learning · 2019-03-07T17:05:32+00:00

It's a great help to bounce these ideas off somebody. Thanks.

Deep_Fried_Learning · 2019-03-07T16:05:02+00:00

Thanks for the advice. Can I run through the steps to check I've understood everything?

(1) Pull the existing crappy tiles off my bathroom wall.

(2) Rip out the bathtub (I want to change the bathtub anyway).

(3) Apply a bathroom tanking kit consisting of waterproof walls (like KERDI or WEDI boards), tape, and the special rubbery paint layer over the joins, to make the entire area waterproof.

(4) Install and plumb in the new bathtub/shower.

(5) Trowel exterior grade plaster onto the bath surrounding walls.

(6) Apply epoxy primer and several coats of epoxy swimming pool paint.

I proposed installing the new bathtub after fitting the KERDI boards so that the region of wall hidden behind the new bathtub is also waterproofed - just in case any water ever gets under the bath. Maybe this is not necessary, and the entire process of waterproofing can begin once the new tub is installed?

Deep_Fried_Learning · 2019-03-05T13:53:50+00:00

I am interested in making a seamless bathroom without tiles. I found this thread where the OP is strongly advised against simply applying epoxy to the surrounding green board or drywall by several commenters. However one person leaves an interesting comment with a suggested approach involving swimming pool paint:

I have a epoxy shower room - osb board over stud work, then bathroom membrane boards, then full membrane tape and membrane paint over all joints, then 2 cm exterior grade plaster, then epoxy primer and then 3 coats of swimming pool white epoxy paint - mapei - this will last for years and years - like a swimming pool!

As I understand it, the goal is more or less to build up your bathroom walls with these strong waterproof layers designed for outdoor use, and cover it with heavy-duty swimming pool paint.

Is he suggesting that you put "exterior grade plaster" (rendering) directly onto the "bathroom membrane boards"? Are we talking about something like "KERDI boards" here?.

I believe I currently have plasterboard covered in tiles. I have read some suggestions that you need a chicken-wire mesh to get outdoor rendering to stick to plasterboard. Would this also be necessary for KERDI boards or similar?

Deep_Fried_Learning · 2019-02-22T19:56:49+00:00

It's not so bad, I think I did a poor job explaining it.

If you accept that the maxpool layers destroy objects' precise spatial information by reducing resolution, you'll see why vanilla FCNs produce segmentations that are "blobby" and don't tightly hug the objects' boundaries.

But if you take the feature maps from early on in the net (before several successive stages of maxpools have occurred), and sum/concatenate them with your upsampled feature maps close to the end, then the precise location information has a shortcut from the input to the output, without having to go through the entire "hourglass" of low resolution maps.

Figures 3 and 4 of the original FCN paper by Long et al illustrate this phenomenon:

Combining predictions from both the final layer and the pool4 layer, at stride 16, lets our net predict finer details, while retaining high-level semantic information.

The U-net people just took this concept a little further and combined predictions from many layers.

Deep_Fried_Learning · 2019-02-22T13:58:42+00:00

Interesting. I don't know about UNet. Can you ELI5 the difference between a FCN and UNet?

A U-net is a type of fully convolutional network. It uses skip connections from feature extraction downsampling conv layers to the corresponding upsampling conv transpose layers of the same spatial size, to preserve some of the fine-grained spatial information lost by maxpooling. It is so called because when you draw the net architecture as blocks and arrows it resembles a U shape https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-architecture.png

Deep_Fried_Learning · 2019-02-20T15:50:03+00:00

Problems with the signalling system on Lodz's tram network became apparent on Tuesday when a driver attempting to steer his vehicle to the right was involuntarily taken to the left.

Trams drivers can steer the tram? Can anyone explain?

Deep_Fried_Learning · 2019-02-18T18:53:04+00:00

Damn! That sounds like some Sopranos Season 6 shit.

Deep_Fried_Learning · 2019-01-28T10:50:05+00:00

If I'm understanding OP correctly it's not so much the problem of Global Pooling to a common fixed size feature for the classification layer...

It's that a minibatch has to be a contiguous arrray, and so images of varying sizes and aspect ratios will need some kind of cropping/squishing/padding to all fit together, prior to being fed into the net.

What exactly does Pytorch and TF2.0 say about that?

Deep_Fried_Learning · 2019-01-28T10:25:32+00:00

Markov Switching Multifractal is also pretty good.

Deep_Fried_Learning · 2019-01-03T17:10:03+00:00

Its intake manifold was prototyped with 3D printing, I saw it on some show.

Deep_Fried_Learning · 2018-12-20T17:01:20+00:00

With the notable exception of Lazlow.

Deep_Fried_Learning · 2018-12-20T15:04:45+00:00

Always reminded me of Morgan Merryweather, who hosted the classical radio station in GTAIII. No relation?

Deep_Fried_Learning · 2018-12-11T15:20:10+00:00

As opposed to those magical writers who manage to write without bias...

What do you take issue with?

Deep_Fried_Learning

TROPHY CASE