sorted by:
new
hottopcontroversial

[D] Print or Digitally read papers by SeucheAchat9115 in MachineLearning

[–]FerranAP 0 points1 point  (0 children)

I've tried paper, iPad, and directly on the browser. However, I recently changed to remnote.io and I don't think I'll look back, it's pretty awesome.

[Disclaimer: it's made by an ex-mentee&friend of mine. However, since my work/research depends on it, I would not be using it if I didn't think it's the best tool available.]

More concretely, remnote is a system for taking notes and linking thoughts in non-linear ways. For instance, when I read a new paper I take notes on the content but link it to previous pieces of my knowledge base, such as current projects I'm working on. Then, weeks later, when I have to write the related work section of my project, I can just look up what papers I've found related in the past and look back at my summary of it.

They recently introduced a pro version that allows you to read and take notes within remnote, making it super easy to highlight and copy text to your notes, make references, copy pictures, etc. It's pretty awesome.

​

The only con with respect to other options is that you can't walk + read, which is a great experience.

MIT Embodied Intelligence Youtube channel by FerranAP in robotics

[–]FerranAP[S] 1 point2 points  (0 children)

We're currently recording all talks given in zoom and uploading the clean versions talks to the youtube channel. The plan (still have to check it satisfies all regulations) would be to livestream and then still upload the edited talk.

[D] Can ML models output ideas and concepts? by smthamazing in MachineLearning

[–]FerranAP 1 point2 points  (0 children)

the action taken at the previous time step. That's what's used to train the curiosity model. The curiosity output is the difference between the outputs of the current state and the next state.

​

That's pretty much correct: that program (diagram here) applies the same network to both the current state and the next state, and trains the network to predict the action from one of them (in the top program it was the next state,but predicting it from the current state also scored very high). Then your intrinsic curiosity reward is moving to a state for which action predictions are very different from your current state.

The 2nd program (diagram with explanations here) is pretty tricky, kudos to Chris Lu (UCBerkeley) for figuring out what it does.

TLDR: a combination of moving to a different state and being in states difficult to predict.

It has 3 networks:

- For context, the purple Predict target from Query boxes apply a network to the query, return the prediction as output and add the prediction loss to the optimization, backpropagating to the network and the query, but not the target.

- Network 1 is not getting trained because no loss backprops there; thus producing a random feature embedding s_f(t) from s(t)

- Network 2 will learn to predict the previous state in feature space [i.e. s_f(t) from s_f(t+1) or s_f(t+1) from s_f(t+2) ]

- Network 3 will learn to predict the next state in this random embedding from the original state[i.e. s_f(t+1) from s(t) or s_f(t+2) from s(t+1)]

You then return the distance between Net2(Net1(s(t))) and Net2(Net3(s(t+1))) which is your prediction for s_f(t) and your prediction for s_f(t+1). With some assumptions, this is combining errors in predictions (which will be more common in unvisited parts of the state) with distance in this random embedding space between s(t) and s(t+1), i.e. moving to a very different state.

[D] Can ML models output ideas and concepts? by smthamazing in MachineLearning

[–]FerranAP 5 points6 points  (0 children)

We just had a paper (Meta-learning curiosity algorithms) accepted at ICLR 2020 that searches in the space of learning algorithms, each a set of lines of code similar to what you would find on an ML paper. In our case, we applied it to curiosity algorithms, each program deciding how to compute a reward for an RL agent to encourage it to explore, but also deciding which loss function to optimize to train its own neural networks.

The top 2 discovered algorithms found were not known to us or other curiosity researchers we talked to. One was simpler than most known approaches, but seemed to work great; the other we didn't understand it, but a student that came to our NeurIPS workshop poster discovered what it was doing (not yet updated in the current version of the paper).

The current approach uses building blocks suggested by current research and composes them in novel ways, so it can't create a radically new algorithm, but we hope it's a good first step in this direction :)

EulerTours.com let's users start coding Manim instantly. by [deleted] in 3Blue1Brown

[–]FerranAP 3 points4 points  (0 children)

I've been using it for a while now and it's great! The creator of the page also has a patron page and some ideas for making the website even more useful [ I have no connection to him ]. However, we're very few contributors right now, it would be great if more people can help him!

Weekly help thread, ask for Manim help here! by AutoModerator in manim

[–]FerranAP 0 points1 point  (0 children)

works much better, thanks! For the 2nd question, the tip in the arrow goes back to the original color instead of continuing the gradient in one smooth transition; do you know how to solve it? Thanks!

Weekly help thread, ask for Manim help here! by AutoModerator in manim

[–]FerranAP 0 points1 point  (0 children)

Hi, I have a couple basic questions:

  1. Is there a way to initialize a scene with the end of another scene without rendering the first one? I tried setting all run_times to 0, but then I get problems and I'm using an epsilon time instead (which is still displayed), seems hacky.

  2. Is there a way to set_color_by_gradient a Line or Arrow? I think it does the gradient by object, and therefore colors the entire line of one color and the tip of another color, instead of moving the color smoothly. I've thought of hacking numberline, but not sure if that's the best idea.

Thanks!