I can't find anywhere else. please give me the Description of Stochastic Reward Net model and the difference between UML & SRN model.
Stochastic Reward Networks (SRN) use Petri nets for what they are trying to achieve. UML's state diagrams also base on Petri nets. The relation between SRN and UML is (at best) like between a rocket and a train. Both are used to transport people.
Related
Can we use Seq2Seq model with input data that has no temporal relation ( not a time series )? For example I have a list of image regions that I would like to feed my seq2seq model. And the the model should predict an description ( output is time series |) or captions.
I’m not asking from the technical perspective, I know that if the data is in the correct format then I can do that. My question is rather theoretical, is it ok to use Seq2Seq with none time series data? And are there any papers/articles/references of using Seq2Seq in this setting ?
No, it just has to be a sequence like requirement.
Klaus Greff, et al., LSTM: A Search Space Odyssey, 2015 :
Since LSTMs are effective at capturing long-term temporal dependencies without suffering from the optimization hurdles that plague simple recurrent networks (SRNs), they have been used to advance the state of the art for many difficult problems. This includes handwriting recognition and generation, language modeling and translation, acoustic modeling of speech, speech synthesis, protein secondary structure prediction, analysis of audio, and video data among others.
Felix A. Gers, et al., Learning to Forget: Continual Prediction with LSTM, 2000 : LSTM holds promise for any sequential processing task in which we suspect that a hierarchical decomposition may exist, but do not know in advance what this decomposition is.
I just finished reading the Transformer paper and BERT paper. But couldn't figure out why Transformer is uni-directional and BERT is bi-directional as mentioned in BERT paper. As they don't use recurrent networks, it's not so straightforward to interpret the directions. Can anyone give some clue? Thanks.
To clarify, the original Transformer model from Vaswani et al. is an encoder-decoder architecture. Therefore the statement "Transformer is uni-directional" is misleading.
In fact, the transformer encoder is bi-directional, which means that the self-attention can attend to tokens both on the left and right. In contrast, the decoder is uni-directional, since while generating text one token at a time, you cannot allow the decoder to attend to the right of the current token. The transformer decoder constrains the self-attention by masking the tokens to the right.
BERT uses the transformer encoder architecture and can therefore attend both to the left and right, resulting in "bi-directionality".
From the BERT paper itself:
We note that in the literature the bidirectional Transformer is often referred to as a “Transformer encoder” while the left-context-only version is referred to as a “Transformer decoder” since it can be used for text generation.
Recommended reading: this article.
A wanna-be data-scientist here and am trying to understand as a data scientist, when and why would you use a Probability Density Function (PDF)?
Sharing a scenario and a few pointers to learn about this and other such functions like CDF and PMF would be really helpful. Know of any book that talks about these functions from practice stand-point?
Why?
Probability theory is very important for modern data-science and machine-learning applications, because (in a lot of cases) it allows one to "open up a black box" and shed some light into the model's inner workings, and with luck find necessary ingredients to transform a poor model into a great model. Without it, a data scientist's work is very much restricted in what they are able to do.
A PDF is a fundamental building block of the probability theory, absolutely necessary to do any sort of probability reasoning, along with expectation, variance, prior and posterior, and so on.
Some examples here on StackOverflow, from my own experience, where a practical issue boils down to understanding data distribution:
Which loss-function is better than MSE in temperature prediction?
Binary Image Classification with CNN - best practices for choosing “negative” dataset?
How do neural networks account for outliers?
When?
The questions above provide some examples, here're a few more if you're interested, and the list is by no means complete:
What is the 'fundamental' idea of machine learning for estimating parameters?
Role of Bias in Neural Networks
How to find probability distribution and parameters for real data? (Python 3)
I personally try to find probabilistic interpretation whenever possible (choice of loss function, parameters, regularization, architecture, etc), because this way I can move from blind guessing to making reasonable decisions.
Reading
This is very opinion-based, but at least few books are really worth mentioning: The Elements of Statistical Learning, An Introduction to Statistical Learning: with Applications in R or Pattern Recognition and Machine Learning (if your primary interest is machine learning). That's just a start, there are dozens of books on more specific topics, like computer vision, natural language processing and reinforcement learning.
I am confused with what a linear chain CRF implementation exactly is. While some people say that "The Linear Chain CRF restricts the features to depend on only the current(i) and previous label(i-1), rather than arbitrary labels throughout the sentence" , some people say that it restricts the features to depend on the current(i) and future label(i+1).
I am trying to understand the implementation that goes behind the Stanford NER Model. Can someone please explain what exactly the linear chain CRF Model is?
Both models would be linear chain CRF models. The important part about the "linear chain" is that the features depend only on the current label and one direct neighbour in the sequence. Usually this would be the previous label (because that corresponds with reading order), but it could also be the future label. Such a model model would basically process the sentence backwards, and I have never seen this in the literature, but it would still be a linear chain CRF).
As far as I know, the Stanford NER model is based on a model that uses the current and the previous label, but it also uses an extension that can also look to labels further back. It is therefore not a strict linear-chain model, but uses an extension described in this paper:
Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363-370. http://nlp.stanford.edu/~manning/papers/gibbscrf3.pdf
I'm trying to write code for A Neural Probabilistic Language Model by yoshua Bengio, 2003, but I'm not able to understand the connections between the input layer and projection matrix and between projection matrix and hidden layer. I'm not able to get how exactly is the learning for word-vector representation taking place.
have a look at this answer here
It explains the difference between the hidden layer and the projection layer.
Referring to this thesis
Also, do read this paper by Tomas Mikolov and go through this tutorial.
this will really improve your understanding.
Hope this helps!