5 ms·

> As far as I can tell, deep learning folks don't give two shits about uncertainty intervals, much less marginalization. All that matters is minimizing that tes

> As far as I can tell, deep learning folks don't give two shits about uncertainty intervals, much less marginalization. All that matters is minimizing that test error as fast as possible. So what if you get a posterior for each parameter... Who cares about the parameters in a neural network as long as the predictions seem well calibrated? Well, the one thing that they ought to actually give a shit about is generalization. All else (Bayesian or not) is only in service of that. Deep learning systems require too much data (and often not raw data), crazy amounts of compute, and still fail under comically irrelevant tweaks of the problem (eg: adversarial noise). Yes, the PAC learning model starts off by assuming that the test set had the same “data distribution” as the train set. It’s obviously not exactly the same (otherwise learning is kinda irrelevant), but deep learning seems incredibly fragile in its ability to generalize. It seems to require crazy amounts of domain randomization during training to avoid overfitting. Eg: The recent OpenAI Rubik’s cube demonstration (while an impressive achievement) was an indication of the fundamental silliness of deep RL (at least as of now). To solve the problem, they had to use classical Rubik’s cube solving algorithms, and completely model the physics of the situation (including friction on the surfaces!) and use that model to generate training data while varying every parameter for domain randomization. All that structured prior knowledge is absolutely unfeasible for even a slightly more complicated situation. And after all that, the robot had something like a 20% success rate IIRC. Impressive work on the team’s part, no doubt... but the conceptual framework does not inspire confidence.

mjburgess 7y ago
I doubt that limits to generalizing NNs will be solved by mere bayesianfication. NNs poorly generalize, as with all of ML, because the strategy is the modelling of the data, not of the world. Since data stands in as proxy for the environment, a NN will only generalize insofar as it has the data to do so. There is no 3rd dimension in an infinite number of 2D photographs -- an no "bayesianification" will put it there.
- jacquesm 7y ago
  > because the strategy is the modelling of the data, not of the world. But that is how all learning works. Even the most advanced physics models are verified using experimental data. If data is found that contradicts the model the model is deemed faulty and will be adjusted to match the insights gained from the new data. DL just automates that approach aiming for the minimum gap between 'theory' and 'data' where the theory is expressed as a number of weights, thus encoding 'insight' in a - for now - non-reversible way. Just like wetware typically has not clue how it arrived at a certain conclusion, we use the words intuition and synonyms when actually we mean 'guess based on past experience'.
  - tsimionescu 7y ago
    That is not how human learning works. Manuals don't contain hundreds of thousands of examples to allow students to draw their own conclusion. And for inventing new physics, there are two very different approaches. One is perhaps similar to machine learning, where people look at an existing model, find experimental data that doesn't match the model, and adjust the model slightly until it matches the new data. But another mode is where you have a model with certain mathematical properties, then you discover new mathatics that give more properties, and you only then ask for more data to check these extensions.
    - Schiphol 7y ago
      How does human learning work, then? Do you have pointers to literature on alternative formal learning theories? I am genuinely curious :)
      - tsimionescu 7y ago
        There's a field called pedagogy which explores some principles of how to teach humans things.
    - smallnamespace 7y ago
      > That is not how human learning works. As someone who has a baby in the house, I assure you that it is indeed how a human learns and refines motor skills, at the very least - by repeated experiment and play, with thousands of permutations of position, speed, weight, and situation. They also try to infer causal models [1]. Just what do you think 'play' is for a child, if not (in part) an opportunity to safely explore its surroundings and refine mental models? [1] https://www.nsf.gov/news/news_videos.jsp?cntn_id=125575&media_id=73196&org https://www.nsf.gov/news/news_videos.jsp?cntn_id=125575&medi...
jacquesm 7y ago
> Deep learning systems require too much data (and often not raw data), crazy amounts of compute, and still fail under comically irrelevant tweaks of the problem (eg: adversarial noise). Many of these criticisms generalize to 'first make it work, then make it better, then make it fast'. Being able to solve some of these problems at all is the kicker, whether or not it is efficient is at the moment not nearly as important as being able to solve them in principle. I suspect that in the very near future we will see something that extracts the actual insights required from a DL model that makes it work which then can be used to power an analytical solution that performs a lot faster than the model itself, and hopefully with more resistance against noise. I've already seen several glimpses of this and I'm hoping for some kind of breakthrough where DL is used to continue where feature engineering left off.
- ssivark 7y ago
  > Many of these criticisms generalize to 'first make it work, then make it better, then make it fast'. Being able to solve some of these problems at all is the kicker, whether or not it is efficient is at the moment not nearly as important as being able to solve them in principle. There are deep (not universally applicable) assumptions in that framing... It works only if you didn't "make it work" in a way that fundamentally limits/handicaps the future steps, or gives no insights in that direction. Based on my understanding of the subject, I think that much recent progress in deep learning is less of a breakthrough than it's commonly made out to be, and that there are fundamental conceptual reasons why the approach is limited, almost like achieving the first step of drawing an owl: https://i.kym-cdn.com/photos/images/newsfeed/000/572/078/d6d.jpg https://i.kym-cdn.com/photos/images/newsfeed/000/572/078/d6d... But the jury is out on that one, and I think there is enough room for reasonable people to differ in their opinions. ¯\_(ツ)_/¯
  - jacquesm 7y ago
    In image classification problems (which covers a lot of ground) for instance the progress is undeniable. But we are still a ways away from extracting the essence and getting to the point where what biology does effortlessly can be done on similar power budgets by a combination of soft and hardware.

mjburgess 7y ago

I doubt that limits to generalizing NNs will be solved by mere bayesianfication. NNs poorly generalize, as with all of ML, because the strategy is the modelling of the data, not of the world. Since data stands in as proxy for the environment, a NN will only generalize insofar as it has the data to do so. There is no 3rd dimension in an infinite number of 2D photographs -- an no "bayesianification" will put it there.

jacquesm 7y ago
> because the strategy is the modelling of the data, not of the world. But that is how all learning works. Even the most advanced physics models are verified using experimental data. If data is found that contradicts the model the model is deemed faulty and will be adjusted to match the insights gained from the new data. DL just automates that approach aiming for the minimum gap between 'theory' and 'data' where the theory is expressed as a number of weights, thus encoding 'insight' in a - for now - non-reversible way. Just like wetware typically has not clue how it arrived at a certain conclusion, we use the words intuition and synonyms when actually we mean 'guess based on past experience'.
- tsimionescu 7y ago
  That is not how human learning works. Manuals don't contain hundreds of thousands of examples to allow students to draw their own conclusion. And for inventing new physics, there are two very different approaches. One is perhaps similar to machine learning, where people look at an existing model, find experimental data that doesn't match the model, and adjust the model slightly until it matches the new data. But another mode is where you have a model with certain mathematical properties, then you discover new mathatics that give more properties, and you only then ask for more data to check these extensions.
  - Schiphol 7y ago
    How does human learning work, then? Do you have pointers to literature on alternative formal learning theories? I am genuinely curious :)
    - tsimionescu 7y ago
      There's a field called pedagogy which explores some principles of how to teach humans things.
  - smallnamespace 7y ago
    > That is not how human learning works. As someone who has a baby in the house, I assure you that it is indeed how a human learns and refines motor skills, at the very least - by repeated experiment and play, with thousands of permutations of position, speed, weight, and situation. They also try to infer causal models [1]. Just what do you think 'play' is for a child, if not (in part) an opportunity to safely explore its surroundings and refine mental models? [1] https://www.nsf.gov/news/news_videos.jsp?cntn_id=125575&media_id=73196&org https://www.nsf.gov/news/news_videos.jsp?cntn_id=125575&medi...

jacquesm 7y ago

> Deep learning systems require too much data (and often not raw data), crazy amounts of compute, and still fail under comically irrelevant tweaks of the problem (eg: adversarial noise). Many of these criticisms generalize to 'first make it work, then make it better, then make it fast'. Being able to solve some of these problems at all is the kicker, whether or not it is efficient is at the moment not nearly as important as being able to solve them in principle. I suspect that in the very near future we will see something that extracts the actual insights required from a DL model that makes it work which then can be used to power an analytical solution that performs a lot faster than the model itself, and hopefully with more resistance against noise. I've already seen several glimpses of this and I'm hoping for some kind of breakthrough where DL is used to continue where feature engineering left off.

ssivark 7y ago
> Many of these criticisms generalize to 'first make it work, then make it better, then make it fast'. Being able to solve some of these problems at all is the kicker, whether or not it is efficient is at the moment not nearly as important as being able to solve them in principle. There are deep (not universally applicable) assumptions in that framing... It works only if you didn't "make it work" in a way that fundamentally limits/handicaps the future steps, or gives no insights in that direction. Based on my understanding of the subject, I think that much recent progress in deep learning is less of a breakthrough than it's commonly made out to be, and that there are fundamental conceptual reasons why the approach is limited, almost like achieving the first step of drawing an owl: https://i.kym-cdn.com/photos/images/newsfeed/000/572/078/d6d.jpg https://i.kym-cdn.com/photos/images/newsfeed/000/572/078/d6d... But the jury is out on that one, and I think there is enough room for reasonable people to differ in their opinions. ¯\_(ツ)_/¯
- jacquesm 7y ago
  In image classification problems (which covers a lot of ground) for instance the progress is undeniable. But we are still a ways away from extracting the essence and getting to the point where what biology does effortlessly can be done on similar power budgets by a combination of soft and hardware.