]> Stephan Baier 1511.07972 Volker Tresp Denis Krompaß 2017-10-24 2017-10-24T14:47:21Z Cristóbal Esteban Learning with Memory Embeddings Yinchong Yang Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Latent variable models are well suited to deal with the high dimensionality and sparsity of typical knowledge graphs. In recent publications the embedding models were extended to also consider time evolutions, time patterns and subsymbolic representations. In this paper we map embedding models, which were developed purely as solutions to technical problems for modelling temporal knowledge graphs, to various cognitive memory functions, in particular to semantic and concept memory, episodic memory, sensory memory, short-term memory, and working memory. We discuss learning, query answering, the path from sensory input to semantic decoding, and the relationship between episodic memory and semantic memory. We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models. 2016-05-07T09:06:15Z Volker Tresp [1511.07972] Learning with Memory Embeddings 2015-11-25T07:06:09Z 2017-10-21 Andrew Moore on "TOKeN: The Open Knowledge Network" - YouTube 2017-10-21T14:31:45Z 2017-10-23T09:05:11Z Using Gensim Word2Vec Embeddings in Keras | Ben Bolte's Blog 2017-10-23 2017-10-25 2017-10-25T14:41:57Z Keras examples directory Keras Embedding Layer requires that the input data be integer encoded, so that each word is represented by a unique integer. This data preparation step can be performed using the Tokenizer API also provided with Keras. The Embedding layer is initialized with random weights and will learn an embedding for all of the words in the training dataset. - Example of Learning an Embedding - Example of Using Pre-Trained GloVe Embedding How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery 2017-10-25T15:40:03Z 2017-10-25 2017-10-22T13:45:32Z How does one apply deep learning to time series forecasting? - Quora 2017-10-22 > I would use the state-of-the-art [recurrent nets](/tag/recurrent_neural_network.html) (using gated units and multiple layers) to make predictions at each time step for some future horizon of interest. The RNN is then updated with the next observation to be ready for making the next prediction Ancient Viruses Are Buried in Your DNA - The New York Times 2017-10-04 2017-10-04T23:52:24Z Les arbres remarquables 2017-10-06T22:20:20Z 2017-10-06 2017-10-19 2017-10-19T22:45:08Z Redirects & SEO - The Complete Guide 303 redirect is never cacheable. Use for - Device targeting - Geo targeting - ... Don't use 303 when - Redirect is permanent - SEO value is supposed to be passed on to the destination URL 2017-10-03T13:58:50Z 2017-10-03 Eric Jang, Research Engineer at Google Brain What product breakthroughs will recent advances in deep learning enable? - Quora 2017-10-24 2017-10-24T10:53:45Z Workshop 17Oct2017 CR (Franck Boudinet) du workshop au Plessis includes description of word2vec 2017-10-01 2017-10-01T19:10:39Z Distributed Word Representations for Information Retrieval 2017-10-04T00:50:36Z 2017-10-04 How to publish data about a range of cars - in 3 slides and 2 links (2013) Named Entity Recognition using Word Embedding as a Feature (2016) 2017-10-01T19:20:07Z 2017-10-01 Uses word embeddings as features for named entity recognition (NER) training, and CRF as learning algorithm 2017-10-21 2017-10-21T16:59:09Z Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017) By incorporating a novel disambiguation algorithm into a state-of-the-art classification model, we create a pipeline to integrate sense-level information into downstream NLP applications. We show that a simple disambiguation of the input text can lead to consistent performance improvement on multiple topic categorization and polarity detection datasets, particularly when the fine granularity of the underlying sense inventory is reduced and the document is sufficiently large. Our results suggest that research in sense representation should put special emphasis on real-world evaluations on benchmarks for downstream applications, rather than on artificial tasks such as word similarity. In fact, research has previously shown that **word similarity might not constitute a reliable proxy to measure the performance of word embeddings in downstream applications** [github](https://github.com/pilehvar/sensecnn) 2017-10-17 The Secret Sex Life of Truffles | CNRS News 2017-10-17T21:53:50Z How to Write a Spelling Corrector (Peter Norvig) 2017-10-25 2017-10-25T23:48:46Z 2017-10-21 2017-10-21T11:55:13Z #PageRank-based #Wikidata autocomplete powered by #Apache #Solr 2017-10-19T00:09:05Z Knowledge Maps: Structure Versus Meaning - DATAVERSITY Our goal is to organize data in the computer the way humans organize data in their minds 2017-10-19 Étoiles à neutrons : une fusion qui vaut de l’or | CNRS Le journal 2017-10-18T13:44:05Z 2017-10-18 2017-10-24T10:20:40Z 2017-10-24 Re: Product Customization as Linked Data - ResearchGate > I found your other paper "A Semantic Web Representation of a Product Range Specification based on Constraint Satisfaction Problem in the Automotive Industry" very interesting. Actually, it is the best paper on this topic I found so far! 2017-10-23T00:19:06Z 2017-10-23 Installing TensorFlow on Mac OS X  |  TensorFlow 2017-10-01 2017-10-01T17:20:12Z “Physique pour tous” | Antoine Tilloy's research log 2017-10-28 maxlath/wikidata-sdk: A javascript tool-suite to query Wikidata and simplify its results 2017-10-28T11:01:29Z 2017-10-06 Product Customization as Linked Data | SpringerLink (ESWC-2012) 2017-10-06T11:25:35Z Text classification using pre-trained GloVe embeddings (loaded into a frozen Keras Embedding layer) and a [convolutional neural network](/tag/convolutional_neural_network) 2017-10-23 2017-10-23T01:07:38Z Using pre-trained word embeddings in a Keras model Watson : l’Intelligence artificielle en ses limites | InternetActu 2017-10-07T21:50:55Z 2017-10-07 2017-10-24 2017-10-24T10:59:04Z "IBM Box" (zone de partage) 2017-10-23 2017-10-23T01:22:35Z A Word2Vec Keras tutorial « Monsanto Papers » : des dérives inadmissibles 2017-10-06T00:40:01Z 2017-10-06 A Beginner's Guide to Recurrent Networks and LSTMs - Deeplearning4j 2017-10-22T13:39:42Z 2017-10-22 Multivariate Time Series Forecasting with LSTMs in Keras - Machine Learning Mastery 2017-10-25T15:58:38Z 2017-10-25 > You can think of it a little bit like you think about Principal Components Analysis, in that it is trained by unsupervised learning so as to capture the leading variations in the data, and it yields a new representation of the data How do RBMs work? - Quora 2017-10-30T12:36:20Z 2017-10-30 Machine Learning Glossary | Google Developers 2017-10-02T13:27:52Z 2017-10-02 Semantic Web Company 2017-10-08 2017-10-08T12:27:46Z Chêne de la Lambonnière 550 ans, Pervenchères (Orne) | Krapo arboricole 2017-10-06T22:31:13Z 2017-10-06 LSTM with word2vec embeddings | Kaggle 2017-10-25T15:50:14Z 2017-10-25 Efficient unsupervised keywords extraction using graphs 2017-10-04T23:01:42Z 2017-10-04 2017-10-21 2017-10-21T11:44:40Z Open Knowledge Network Command-line tool to extract taxonomies from Wikidata. 2017-10-29 2017-10-29T09:09:26Z wikidata-taxonomy 2017-10-25 2017-10-25T22:56:55Z Un correcteur orthographique en 21 lignes de Python 2017-10-16 2017-10-16T14:34:28Z Tensorflow sucks see [What do people think of the TensorFlow sucks article? on Quora](https://www.quora.com/What-do-people-think-of-the-TensorFlow-sucks-article) wikimedia/wikidata-query-gui the GUI for the Wikidata Query Service. 2017-10-28 2017-10-28T11:04:18Z 2017-10-23T08:53:16Z Recurrent neural networks and LSTM tutorial in Python and TensorFlow - Adventures in Machine Learning 2017-10-23 facebookresearch/fairseq-py: Facebook AI Research Sequence-to-Sequence Toolkit written in Python. 2017-10-02T13:34:19Z 2017-10-02 The Neural Network Zoo - The Asimov Institute 2017-10-07T11:12:32Z 2017-10-07 2017-10-22T13:56:59Z Distilling Free-Form Natural Laws from Experimental Data | Science 2017-10-22 Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation Headhunters (film) 2017-10-07T01:13:54Z 2017-10-07 Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems - Microsoft Research (2015) 2017-10-02T00:09:19Z 2017-10-02 > new simple, yet effective approaches to learn domain specific word embeddings. ## Intro > Adapting word embeddings, such as jointly capturing syntactic and semantic information, can further enrich semantic word representations for several tasks, e.g., sentiment analysis (Tang et al. 2014), named entity recognition (Lebret, Legrand, and Collobert 2013), entity-relation extraction (Weston et al. 2013), etc. (Yu and Dredze 2014) has introduced a lightly supervised word embedding learning extending word2vec. They incorporate prior information to the objective function as a regularization term considering synonymy relations between words from Wordnet (Fellbaum 1999). > In this work, we go one step further and investigate if enriching the word2vec word embeddings trained on unstructured/ unlabeled text with domain specific semantic relations obtained from knowledge sources (e.g., knowledge graphs, search query logs, etc.) can help to discover relation aware word embeddings. Unlike earlier work, **we encode the information about the relations between phrases, thereby, entities and relation mentions are all embedded into a low dimensional vector space**. ## Related work (Learning Word Embeddings with Priors) - word2vec - Relational Constrained Model (RTM) (Yu and Dredze 2014) While CBOW learns lexical word embeddings from provided text, the RTM learns embeddings of words based on their similarity to other words provided by a knowledge resource (eg. wordnet) - Joint model (Yu and Dredze 2014) combines CBOW and RTM through linear combination Le vénérable chêne de La Loupe, Meaucé (Eure-et-Loir) | Krapo arboricole 2017-10-06T22:19:08Z 2017-10-06 301 Redirects Rules Change: What You Need to Know for SEO - Moz 2017-10-19T22:35:06Z 2017-10-19 2017-10-24T14:44:20Z Volker Tresp 2015-09-28T17:40:35Z 1503.00759 Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's Knowledge Vault project as an example of such combination. Maximilian Nickel [1503.00759] A Review of Relational Machine Learning for Knowledge Graphs Kevin Murphy A Review of Relational Machine Learning for Knowledge Graphs Evgeniy Gabrilovich 2017-10-24 2015-03-02T21:35:41Z Maximilian Nickel 2017-10-01 2017-10-01T17:20:30Z Gravity may be created by strange flashes in the quantum realm | New Scientist AlphaGo Zero: Learning from scratch | DeepMind 2017-10-18T22:43:19Z 2017-10-18 2017-10-16 2017-10-16T18:00:46Z Les ondes gravitationnelles font la première lumière sur la fusion d'étoiles à neutrons - Communiqués et dossiers de presse - CNRS Intelligence artificielle : toujours plus puissant, AlphaGo apprend désormais sans données humaines 2017-10-18T22:38:12Z 2017-10-18 2017-10-14 Marc'Aurelio Ranzato State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available. [1710.04087] Word Translation Without Parallel Data Ludovic Denoyer 1710.04087 Guillaume Lample Hervé Jégou 2017-10-14T13:56:33Z Word Translation Without Parallel Data 2017-10-11T14:24:28Z Alexis Conneau 2018-01-30T14:41:51Z Alexis Conneau > we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way 2017-10-20 2017-10-20T15:37:22Z Myanmar, Once a Hope for Democracy, Is Now a Study in How It Fails - The New York Times