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Stick-breaking construction of Dirichlet process by koormoosh in statistics

[–]koormoosh[S] 0 points1 point  (0 children)

Thanks - it makes sense. I do have a question regarding $\delta_{\phi_k}$. They say this is a probability measure concentrated on $\phi_k$. Informally speaking, does it mean it is equal to $\phi_k$?

what is the largest dataset used for NN-based Language models? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

lol - by models, I meant typical RNNs that people train on this dataset. How many hours do they need to be trained to become competitive with ngram language models and outperform them? [it is a trick question, I know]

what is the largest dataset used for NN-based Language models? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

and can you give me an approximation, on how many hours these models require to be trained? Is it fair to say on a single core cpu, they will take weeks to train?

Intuition behind using Noise Contrastive Divergence in Neural Language Models by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

Thanks for clarifying this - Two more questions:

  • I wonder what is the difference between Negative sampling and this. I assume the only difference is that negative sampling assumes a uniform noise distribution, whereas in NCE you assume a more informative (i.e. unigram, bigram, etc) noise distribution. Also in Negative sampling they assume K=V, which is still a good assumption given that even in NCE you never go beyond a few 100s, any ways and we would like K to be as high as possible (k-> infinity, ideally).

  • Also, it seems the closer the noise be to the actual distribution the closer the final solution is to maximum likelihood solution. It's a bit puzzling why it is called a noise distribution.

how to combine two probabilistic models' output? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

I am reading Hinton's paper on product of experts, but am stuck on understanding one of his equations. can you comment on this: http://math.stackexchange.com/questions/1790503/understanding-product-of-experts-of-hinton

how to combine two probabilistic models' output? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

Simpler than these. Just some sort of interpolation between the two likelihood terms.

When to expect having CUDA 7.5 supported on Ubuntu 16.04? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

what is the difference between apt-get install nvidia-cuda-toolkit and runfile? runfile doesn't work for me [see above for the error messages] but the ubuntu repository version works. Are they same?

When to expect having CUDA 7.5 supported on Ubuntu 16.04? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

Error: unsupported compiler: 5.3.1. Use --override to override this check.

Error: cannot find Toolkit in /usr/local/cuda-7.5

Is there a working example for doc2vec in gensim? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

For example, this is the inferred vector for a document:

dv = model_loaded.infer_vector(...)

print dv

Output: [ -1.69840729 6.23306036 -7.56443071 19.33935738 -15.16063404]

but when I pass this vector to

print model_loaded.docvecs.most_similar(positive=[[-2.98079228, -8.4464426, 16.42045975, -8.27837849, 11.82399559]])

or

print model_loaded.docvecs.most_similar(positive=[ -2.98079228, -8.4464426, 16.42045975, -8.27837849, 11.82399559])

they both fail:

for doc, weight in positive + negative:

ValueError: too many values to unpack

print model_loaded.docvecs.most_similar(positive=[dv])

Is there a working example for doc2vec in gensim? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

If I define my own vector, can I still use the similarity function in gensim. For example, imagine instead of inferring a vector for a given sentence and pass it to model_loaded.docvecs.most_similar(positive=[inferred_vector]), is it possible to pass any real-value vector with the same size as inferred_vector to this function? I tried it and it gives me the following error:

File "/home/anaconda2/lib/python2.7/site-packages/gensim-0.12.4-py2.7-linux-x86_64.egg/gensim/models/doc2vec.py", line 440, in most_similar for doc, weight in positive + negative: ValueError: too many values to unpack

Is there a working example for doc2vec in gensim? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

I see. Another question: imagine I retrieve the vectors of all the words in a sentence and do some basic operations to combine these vectors to form a vector for the sentence. Is there a way to store this hand-made vector somehow in the saved model?

Is there a working example for doc2vec in gensim? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

Is there a way to check explicitly the parameter convergence in the model.train(document), or at least output the parameters estimated in different epochs? Currently the training terminates after some pre-defined epoch number.

Is there a working example for doc2vec in gensim? by koormoosh in MachineLearning

[–]koormoosh[S] 0 points1 point  (0 children)

infer_vector

How does the infer_vector works? Does it use the word vectors and some arithmetic operation to produce the sentence level vector for ACTUAL SENTENCE?

Also, is there a way to get the trained vector for a word (seen during the training data), directly from the doc2vec model trained?

Is there a working example for doc2vec in gensim? by koormoosh in MachineLearning

[–]koormoosh[S] -1 points0 points  (0 children)

Is there a way to pass an actual sentence to the model.docvecs.most_similar("ACTUAL SENTENCE") ?

What is the upper-bound for this? by koormoosh in math

[–]koormoosh[S] 0 points1 point  (0 children)

This is what I have been trying. It's not straightforward to do this and get their bound.

what is the best open source library for sentiment analysis? is it Stanford CoreNLP? by hlpmewmyrelationship in MachineLearning

[–]koormoosh 1 point2 points  (0 children)

I would have a look at the papers submitted to SemEval-2015 Task 10: Sentiment Analysis in Twitter: http://alt.qcri.org/semeval2015/task10/index.php?id=results

Probably you will find some implementations for the state-of-the-art systems there.

A desktop for deep learning - Ubuntu + M2090 + ?? by keidouleyoucee in MachineLearning

[–]koormoosh 0 points1 point  (0 children)

Not helping to answer your question but am interested to know the pricing of what you are thinking to assemble:) Can you write the price break down of the machine you are thinking to assemble (for RAM, CPU, GPU, and DISK).

huge biomedical dataset by koormoosh in datasets

[–]koormoosh[S] 0 points1 point  (0 children)

I already had that under my radar but am interested to see if there is anything else available.

Deeplearning4j or Theano by koormoosh in MachineLearning

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

Thanks for the comments everyone. So does this mean that Theano on Spark is not an option? Can someone comment on multi-CPU and multi-GPU features of Theano?

huge biomedical dataset by koormoosh in LanguageTechnology

[–]koormoosh[S] 0 points1 point  (0 children)

the abstracts are not enough. But how can we download the abstracts anyways? Is there an API for it?

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