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It really is a great paper, the problem is when your entire field has few real applications but everyone is making too much money to admit that. Cool tech demos

It really is a great paper, the problem is when your entire field has few real applications but everyone is making too much money to admit that. Cool tech demos like this is awesome as a blog post, but not sure why it's academic...

YeGoblynQueenne 6y ago
Well, the paper has an experiment where they train an agent based on their system to play two of the three games (PacMan, PacManMaze and VizDoom) and compare its result to two other systems (an LSTM-based one and a model-based RL one, WorldModel). Their system gets the best score in Pacman but unfortunately the version that does better than WorldModel is the one that doesn't have the memory module whose usefulness is a central claim of their paper. WorldModel outperforms their system in VizDoom by ~300 points, though their system without the memory module "solves" VizDoom (i.e. scores just a bit over than 750; that is 765) and they claim that it's the first Gan-based system that achieves this. A bit thin of the ground (especially when it comes to supporting the claim about the usefulness of the memory module) but the way neural nets research and especially RL research is going (from what I've seen, not my area of expertise) that's the kind of result that you can expect will get your paper published these days.
esoterica 6y ago
I don't know where you got the idea that the purpose of an academic paper is to produce the schematics for a working commercial product. The fact that it's novel and interesting is sufficient grounds for producing a paper about it. Why do you think projects like these are solely the domain of blog posts?
- YeGoblynQueenne 6y ago
  That is not true for academic papers in computer science and particularly in machine learning were contributions are expected to have some sort of practical application that is at least possible to foresee. Indeed, a common criticism against papers in machine learning is "what is a real-world application of this approach?". If you can't answer that convincingly then it's unlikely your paper gets published, particularly if it has rather mediocre results otherwise, as is the case with this paper (see my earlier comment about the comparison to the two other systems which is a bit of an anticlimax). In fact, the paper (well, the preprint version) makes a clear attempt to motivate the work with a practical application, saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment".
  - esoterica 6y ago
    > That is not true for academic papers in computer science and particularly in machine learning were contributions are expected to have some sort of practical application that is at least possible to foresee. This is 100% not true for computer science and I don't know why you think it's true. There's are entire branches of computer science (e.g. complexity theory) that are highly theoretical and are certainly not geared towards immediate practical application. Even in more applied branches of computer science like ML there are many papers that are geared more towards the fundamental research side of things. > saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment". Saying that a paper could lead to a useful application down the line is not the same that saying that the paper is intended to describe the schematics for a specific useful application.
    - YeGoblynQueenne 6y ago
      Well of course, it's not 100% true- who said anything about 100%? There are theoretical branches of computer science; but most of computer science is not purely theoretical. Certainly machine learning (and more broadly speaking, much of AI) is like I say. I'm not sure who brought up a schematic? The bit I quoted is taken from the paper and it's a clear attempt to motivate the paper's approach as having a practical application. To clarify, do you disagree with that?

YeGoblynQueenne 6y ago

Well, the paper has an experiment where they train an agent based on their system to play two of the three games (PacMan, PacManMaze and VizDoom) and compare its result to two other systems (an LSTM-based one and a model-based RL one, WorldModel). Their system gets the best score in Pacman but unfortunately the version that does better than WorldModel is the one that doesn't have the memory module whose usefulness is a central claim of their paper. WorldModel outperforms their system in VizDoom by ~300 points, though their system without the memory module "solves" VizDoom (i.e. scores just a bit over than 750; that is 765) and they claim that it's the first Gan-based system that achieves this. A bit thin of the ground (especially when it comes to supporting the claim about the usefulness of the memory module) but the way neural nets research and especially RL research is going (from what I've seen, not my area of expertise) that's the kind of result that you can expect will get your paper published these days.

esoterica 6y ago

I don't know where you got the idea that the purpose of an academic paper is to produce the schematics for a working commercial product. The fact that it's novel and interesting is sufficient grounds for producing a paper about it. Why do you think projects like these are solely the domain of blog posts?

YeGoblynQueenne 6y ago
That is not true for academic papers in computer science and particularly in machine learning were contributions are expected to have some sort of practical application that is at least possible to foresee. Indeed, a common criticism against papers in machine learning is "what is a real-world application of this approach?". If you can't answer that convincingly then it's unlikely your paper gets published, particularly if it has rather mediocre results otherwise, as is the case with this paper (see my earlier comment about the comparison to the two other systems which is a bit of an anticlimax). In fact, the paper (well, the preprint version) makes a clear attempt to motivate the work with a practical application, saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment".
- esoterica 6y ago
  > That is not true for academic papers in computer science and particularly in machine learning were contributions are expected to have some sort of practical application that is at least possible to foresee. This is 100% not true for computer science and I don't know why you think it's true. There's are entire branches of computer science (e.g. complexity theory) that are highly theoretical and are certainly not geared towards immediate practical application. Even in more applied branches of computer science like ML there are many papers that are geared more towards the fundamental research side of things. > saying that it aims to "learn a simulator [for robotic agents] by simply watching an agent interact with an environment". Saying that a paper could lead to a useful application down the line is not the same that saying that the paper is intended to describe the schematics for a specific useful application.
  - YeGoblynQueenne 6y ago
    Well of course, it's not 100% true- who said anything about 100%? There are theoretical branches of computer science; but most of computer science is not purely theoretical. Certainly machine learning (and more broadly speaking, much of AI) is like I say. I'm not sure who brought up a schematic? The bit I quoted is taken from the paper and it's a clear attempt to motivate the paper's approach as having a practical application. To clarify, do you disagree with that?