[HN Gopher] SynJax: Jax library for efficient probabilistic mode...
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       SynJax: Jax library for efficient probabilistic modeling of
       structured objects
        
       Author : pizza
       Score  : 52 points
       Date   : 2023-08-08 14:24 UTC (2 days ago)
        
 (HTM) web link (twitter.com)
 (TXT) w3m dump (twitter.com)
        
       | lordofgibbons wrote:
       | Could anyone please explain what this would be used for? Does
       | this project define NNs that are designed for datasets with a
       | known structure?
        
         | Q6T46nT668w6i3m wrote:
         | It's useful when you're modeling a structure, e.g., P(x, y) or
         | P(x | y), rather than a distribution. This is helpful in
         | situations where generating samples is bad (CRFs) or your data
         | imposes dependent features (HMMs). It appears from the README
         | (and the authors' previous work) this is useful for arbitrary
         | structures too.
        
         | gh02t wrote:
         | The things in SynJax aren't neural nets themselves as far as I
         | see poking around, it's a bunch of different [complicated]
         | probability distributions that you might encounter when
         | modeling neural nets or related tasks stochastically. More of a
         | toolbox of components implemented in JAX for advanced
         | statistical modeling inside of a NN. Could also be useful apart
         | from NNs, too.
        
           | nextos wrote:
           | Yes, that is confusing as the intro in the README says
           | "SynJax is a neural network library for JAX structured
           | probability distributions".
           | 
           | However, contrary to my expectations after reading that, they
           | do not have lots primitives to mix neural and probabilistic
           | models.
           | 
           | They seem to be heading in that direction, with e.g.
           | connectionist temporal classification (CTC).
           | 
           | Still very interesting library, seems to provide massive
           | efficiency gains for difficult generative models such as
           | PCFGs.
        
         | 6gvONxR4sf7o wrote:
         | This is probably a good start
         | https://en.wikipedia.org/wiki/Graphical_model
         | 
         | It's traditionally fairly challenging to implement a relatively
         | straightforward graphical model, but automatic differentiation
         | changed that. Some people are working on getting the best of
         | graphical models and black box models (like neural networks),
         | which is where things like this come in.
         | 
         | Because it's jax, you get autograd and gpu/tpu acceleration for
         | free, which further lets you stick these things in with other
         | models, including black box models like NNs.
         | 
         | An example application is in sentence part of speech tagging
         | (noun, adjective, etc). You could model each word's part of
         | speech as a separate prediction, but you know that "noun-verb-
         | noun" is more common than "noun-noun-noun" so you know your
         | predictions should influence each other. Stuff like this makes
         | that easier.
         | 
         | edit:
         | 
         | The authors of this library also put a paper on arxiv
         | describing it https://arxiv.org/pdf/2308.03291.pdf The abstract
         | gives a good sense of what they're going after:
         | 
         | > The development of deep learning software libraries enabled
         | significant progress in the field by allowing users to focus on
         | modeling, while letting the library to take care of the tedious
         | and time-consuming task of optimizing execution for modern
         | hardware accelerators. However, this has benefited only
         | particular types of deep learning models, such as Transformers,
         | whose primitives map easily to the vectorized computation. The
         | models that explicitly account for structured objects, such as
         | trees and segmentations, did not benefit equally because they
         | require custom algorithms that are difficult to implement in a
         | vectorized form. SynJax directly addresses this problem by
         | providing an efficient vectorized implementation of inference
         | algorithms for structured distributions covering alignment,
         | tagging, segmentation, constituency trees and spanning trees.
         | 
         | > With SynJax we can build large-scale differentiable models
         | that explicitly model structure in the data. The code is
         | available at https://github.com/deepmind/synjax.
        
       | uoaei wrote:
       | Link should go to: https://github.com/deepmind/synjax
       | 
       | The repo isn't even linked in the X post, which is strange.
        
         | sdfghswe wrote:
         | It's not an "X post", it's a tweet.
        
           | [deleted]
        
         | sampo wrote:
         | > The repo isn't even linked in the X post
         | 
         | It's a thread of 4 tweets. Twitter has a limit of 280
         | characters per tweet, so usually people split their text into
         | multiple tweets. The link is in the 4th one, as you might
         | expect.
         | 
         | https://nitter.net/milosstanojevic/status/168889655879052083...
        
           | uoaei wrote:
           | I'm logged out and have adblockers on. I used to be able to
           | see the posts that come after, but now I only see the first
           | one. Maybe that's a new change.
           | 
           | Thanks for the nitter link.
        
             | zote wrote:
             | For anyone else not in the know, Twitter(X?) now limits
             | your view to only the tweet a link points to, if you're not
             | logged in.
        
           | throwaway290 wrote:
           | > Twitter has a limit of 280 characters per tweet
           | 
           | I have seen much longer posts on there, like a full screen of
           | text... how come?
        
             | Iwan-Zotow wrote:
             | I think if you pay, you'll get more
        
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       (page generated 2023-08-10 23:01 UTC)