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Minh's virtual lab

Minh's virtual lab. Notes on adversarial network. These days, I heard a lot about un-/semisupervised learning. Ivan Titov’s workshop. At the beginning of this month is all about it and now Facebook. June 21, 2016. Hyperparameter tuning in SURFsara HPC Cloud. Continue reading →. June 17, 2016. Reproducing Chen and Manning (2014). Though Stanford open-sourced their parser as part of CoreNLP, they didn’t release the code of their. As anybody in academia probably knows, reproducing experiments is non-trivial...

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Minh's virtual lab | minhlab.wordpress.com Reviews

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Minh's virtual lab. Notes on adversarial network. These days, I heard a lot about un-/semisupervised learning. Ivan Titov’s workshop. At the beginning of this month is all about it and now Facebook. June 21, 2016. Hyperparameter tuning in SURFsara HPC Cloud. Continue reading →. June 17, 2016. Reproducing Chen and Manning (2014). Though Stanford open-sourced their parser as part of CoreNLP, they didn’t release the code of their. As anybody in academia probably knows, reproducing experiments is non-trivial...

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Minh's virtual lab | minhlab.wordpress.com Reviews

https://minhlab.wordpress.com

Minh's virtual lab. Notes on adversarial network. These days, I heard a lot about un-/semisupervised learning. Ivan Titov’s workshop. At the beginning of this month is all about it and now Facebook. June 21, 2016. Hyperparameter tuning in SURFsara HPC Cloud. Continue reading →. June 17, 2016. Reproducing Chen and Manning (2014). Though Stanford open-sourced their parser as part of CoreNLP, they didn’t release the code of their. As anybody in academia probably knows, reproducing experiments is non-trivial...

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Similarity, co-occurrence, functional relation, part-whole relation, subcategorization, what else? | Minh's virtual lab

https://minhlab.wordpress.com/2015/03/18/relatedness-madness

Minh's virtual lab. Similarity, co-occurrence, functional relation, part-whole relation, subcategorization, what else? In word sense disambiguation and named-entity disambiguation, an important assumption is that a document consists of related concepts and entities. There are millions of concepts and entities, what makes some related but not others? This question is difficult and I don’t have the definitive answer. But it is a good start to list some classes of relatedness. Have been used in place of.

2

Skip-gram negative sampling as (unshifted) PMI matrix factorization | Minh's virtual lab

https://minhlab.wordpress.com/2015/10/18/skip-gram-negative-sampling-as-unshifted-pmi-matrix-factorization

Minh's virtual lab. Skip-gram negative sampling as (unshifted) PMI matrix factorization. We arrived at two formulas showing the equivalence between SGNS and shifted PMI:. Apparently, the reason for the “shift” is that in (1) there’s no. While in (2) there is. The “shift” is not just an ugly patch in the formula but it might also have a negative effect on the quality of learned embeddings. The solution is easy: replace. In the original Google’s source code into:. October 18, 2015. Enter your comment here.

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A new proof of the equivalence of word2vec’s SGNS and Shifted PPMI | Minh's virtual lab

https://minhlab.wordpress.com/2015/06/08/a-new-proof-for-the-equivalence-of-word2vec-skip-gram-and-shifted-ppmi

Minh's virtual lab. A new proof of the equivalence of word2vec’s SGNS and Shifted PPMI. My answer: Yes, there is. Recall that negative sampling is all about relative probability between two distribution: positive. After sampling a word-context pair (. From either distribution with the ratio D:N=1:. It is the job of a model to guess which distribution the pair came from. (UPDATE: It is perhaps more prevalent to think of. As conditional distributions, see alternative proof. Bayes rule is your friend:.

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minhlab | Minh's virtual lab

https://minhlab.wordpress.com/author/minhlab

Minh's virtual lab. Long paper accepted for EACL 2017. Title: Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing. European Chapter of the Association for Computational Linguistics), at Valencia, 3-7 April 2017. Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing. By Minh Le and Antske Fokkens. March 7, 2017. Presentation at CLIN 27. February 16, 2017. Notes on adversarial network. June 21, 2016. Continue reading →.

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Notes on adversarial network | Minh's virtual lab

https://minhlab.wordpress.com/2016/06/21/notes-on-adversarial-network

Minh's virtual lab. Notes on adversarial network. These days, I heard a lot about un-/semisupervised learning. Ivan Titov’s workshop. At the beginning of this month is all about it and now Facebook. But wr.t. artificial intelligence, I have my doubts. Generating realistic images is an artificial task. Try to imagine your house in your mind. Most likely, the image won’t have much details. Now try to draw it on a paper. Try to draw even the simplest object, a coin, a book cover or a p...But that’s no...

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understandinglanguagebymachines.org

— Similarity & Relatedness | Understanding Language by Machines

http://www.understandinglanguagebymachines.org/similarity-relatedness

July 1, 2015. Presentations by Marten Postma, Minh Le, Alessandro Lopopolo, and Emiel van Miltenburg. July 01, 2015 at VU Amsterdam. Similarity and relatedness lie at the core of semantics. At CLTL, we study similarity and relatedness from different perspectives: linguistic perspective using WordNet hierarchy, mathematic perspective about evaluation, neurological perspective using brain wave recoding, and perceptual perspective using distributional and multimodal approach. I present a series of neuro-ima...

understandinglanguagebymachines.org

A quantum model of text understanding | Understanding Language by Machines

http://www.understandinglanguagebymachines.org/a-quantum-model-of-text-understanding

A quantum model of text understanding. July 17, 2014. Within ULM-4, Minh Lê & Filip Ilievski investigate a new model of natural-language-processing. Current approaches are based on a pipeline architecture…. Understanding language by machines. 8216;A quantum model of text understanding’ by: Minh Ngoc Lê, MSc. 038; Filip Ilievski, MSc. Figure: Forward propagation for an example: (cat, has part, tail). Minh Ngoc Lê’s presentation Dec. 10 2014. Joint neural embeddings of synsets and entities.

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People | CLTL

http://www.cltl.nl/people

Computational Lexicology and Terminology Lab. Centres & Associations. Dutch Terminology Service Centre. Centre for Digital Humanities Amsterdam. Language, Knowledge and People in Perspective. Reading between the lines. Visualizing Uncertainty and Perspectives. Understanding of Language by Machines (ULM). VU University Research Fellow (URF). Can we Handle the News (EYR4). Mapping Notes and Nodes in Networks. From sentiments and opinions in texts to positions of political parties. The VU Sound Corpus.

understandinglanguagebymachines.org

People | Understanding Language by Machines

http://www.understandinglanguagebymachines.org/people

Prof dr. Piek Vossen. Dr PEM. Huygen. Filip Ilievski, MSc. PhD Student [ GitHub. Dr Rubén Izquierdo Beviá. Senior Researcher [ GitHub. Minh Ngoc Lê, MSc. PhD Student [ GitHub. Emiel van Miltenburg, MA. PhD Student [ GitHub. Dr Roser Morante Vallejo. Senior Researcher [ Google Scholar. Marten Postma, MA. PhD Student [ GitHub. Chantal van Son, MA. Leave a Reply Cancel reply. Your email address will not be published. Required fields are marked *. Jun 24 2016: 2nd Spinoza workshop: Looking at the Long Tail.

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Minh's virtual lab

Minh's virtual lab. Notes on adversarial network. These days, I heard a lot about un-/semisupervised learning. Ivan Titov’s workshop. At the beginning of this month is all about it and now Facebook. June 21, 2016. Hyperparameter tuning in SURFsara HPC Cloud. Continue reading →. June 17, 2016. Reproducing Chen and Manning (2014). Though Stanford open-sourced their parser as part of CoreNLP, they didn’t release the code of their. As anybody in academia probably knows, reproducing experiments is non-trivial...

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