supercarrot
Joined 10 years ago
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Comment · 10 years ago
Comment · 10 years ago
fracking - 70 to 140 billion gallons anually
animal agriculture - 34 to 76 trillion gallons anually
at least we know what wastes a lot of water, the solution is simple, but is not as tasty.
as for heating, yes, it's quite a problem.
withouthotair.com is a great resource for the approximate numbers.
Comment · 10 years ago
This is implied through research on reductions in machine learning.
That simple models can solve complex tasks.
For example, you can do multiclass classification, cost sensitive (importance weighted) multiclass and binary, quantile regression, structured prediction (as is done with HMMs, CRFs, MEMMs, structured SVMs etc.), just using a binary classifier.
So, if your implementation of that binary classifier is efficient and performant, you'll be (given that your reduction is consistent) efficient and performant on any of the above tasks.
What the authors of the paper above did, is that they rediscovered some old tricks, removed the theory of reductions, and that's that - without referencing vowpal wabbit that does way more useful tricks. I'm not sure why, because VW team consistently references Leon Bottou (out of all others) that is member of FAIR, and has been using implementation tricks for decades.
Their log(k) implementation is probably less performant than the one-against-some consistent reduction in VW due to the latter having better theoretical bounds on performance.
Comment · 10 years ago
as much as it's not part of the standard, __restrict__ allows limiting the effects of pointer aliasing in C/C++. [1]
Comment · 10 years ago
> IMO RNNs do need some kind of structured loss (more than per step likelihood) to be competitive with HMM approaches using Viterbi decoding
This is exactly what they do with the dependency parser I've cited, so your opinion is definitely valid. Although their approach is not general, given the fact that they approximate hamming loss with log-loss and again make it work only on sequences.
http://arxiv.org/abs/1502.02206
paper above also has a very good analysis on how to remove the search component of the inference and allow linear time complexity with competitive results.
consistent (as it is used in the machine learning theory of reductions) reduction from structured learning to multiclass classification seems to be possible. I just haven't seen anyone couple the learning procedure with neural networks. (Daume did mention they trained RNNs with the reductionist approach but seems that the code didn't make it to vowpal wabbit).
the approach above works with any loss you want (from F-score to any weird thing you might think of), the loss doesn't have to decompose over the structure (one can just announce the loss after the labelling is done and learn from that loss), it can work on any kind of structure, from images to sequences to documents for translation. it can also use a O(log n) consistent reduction of multiclass classification if speed is of the issue and if number of classes is large. It can easily work as an online method too, not requiring the full structured input.
for example, simple sequence tagging works (depending on the number of possible labels) around 500k tokens per second :D word count is only 2-4 times faster than that :D
there still aren't any papers using the above consistent reduction in the framework of NNs but I guess they'll soon be coming.
Comment · 10 years ago
Still, beam search can be simulated and improved by running decision processes in parallel.
For example, instead of learning the sequence labeling as a sequence of n decisions (where n is the length of the sequence) you can learn sequence labeling as a sequence of 3n+1 decisions where you make 3 decisions for each sequence element and after 3n decision pick one out of three decision streams that minimizes loss using an extra decision. (when inference is done then the classifier will, hopefully, pick the stream that minimizes test loss).
This simulates a beam search and can be done during learning and inference and is probably more effective than picking confidence scores of particular decisions and keeping a beam of most confident partial sequences.
Bean search is a heuristic thing that improves performance and is done mostly to allow you to correct mistakes you made at the beginning of the process.
http://arxiv.org/abs/1603.06042
they illustrate the problem well (label bias).
the question remains the same, for example, in the paper above they approximate the partition function of CRFs with a beam but get superior results to other structured prediction methods.
Animal agriculture is being done everywhere.
I'm doubting people are eating meat from Brazil, Australia and Denmark in US, Asia or Africa so much that these are the main dots of world animal agriculture.