]> 2018-04-05T01:50:39Z 2018-04-05 A short introduction to NLP in Python with spaCy – Towards Data Science 2018-04-26T08:17:19Z « Le Web a développé des résistances antibiotiques à la démocratie » 2018-04-26 2018-04-09 2018-04-09T10:46:54Z Using Celery With Flask - miguelgrinberg.com Goal: to enhance DL systems with reasoning capabilities from the ground-up [Abstract](https://cs.unc.edu/tcsdls/tcsdls-bios-abstracts-2017-2018/#Manning) - allowing them to perform transparent multi-step reasoning processes - while retaining end-to-end differentiability and scalability to real-world problems > I get the feeling that if we're going to make further progress in AI, we actually have to get back to some of these problems of knowledge representation reasoning - From ML to machine reasoning - the CLEVR task - Memory-Attention-Composition Networks What is reasoning? (Bottou 2011) - manipulating previously acquired knowledge in order to answer a question - not necessarily achieved by making logical inference (eg: algebraic manipulations of matrices) - composition rules -> combination of operations to address new tasks 2018-04-18 2018-04-18T00:14:39Z Christopher Manning - "Building Neural Network Models That Can Reason" (TCSDLS 2017-2018) - YouTube 2018-04-08T15:28:14Z 2018-04-08 Sense2vec with spaCy and Gensim · Blog · Explosion AI PROCEEDINGS – The Web Conference in Lyon 2018-04-23T17:33:50Z 2018-04-23 Course Project Reports for 2018. [Notes on reddit](https://www.reddit.com/r/MachineLearning/comments/89i9h8/ps_the_2018_stanford_cs224n_nlp_course_projects/) CS224n: Natural Language Processing with Deep Learning 2018-04-05T01:55:59Z 2018-04-05 2018-04-29T23:07:54Z CNRS INSIS - Un pas vers les puces miniatures intelligentes 2018-04-29 Essential Guide to keep up with AI/ML/CV 2018-04-06T16:16:13Z 2018-04-06 2018-04-05T01:57:50Z 2018-04-05 Exploring neural architectures for NER (CS224N 2018) China wants to be the world leader in artificial intelligence by 2030. To get there, it's reinventing the way children are taught: evolving from a model in which the mastery of routine skills is the end of education, to one in which they’re a means to the end of creative inquiry 2018-04-21 2018-04-21T11:25:45Z China’s children are its secret weapon in the global AI arms race | WIRED UK **Best paper award** at theWebConf 2018. An approach to harvest higher-arity facts from textual sources. Our method is distantly supervised by seed facts, and uses the fact-pattern duality principle to gather fact candidates with high recall. For high precision, we devise a constraint-based reasoning method to eliminate false candidates. A major novelty is in coping with the difficulty that higher-arity facts are often expressed only partially in texts and strewn across multiple sources. For example, one sentence may refer to a drug, a disease and a group of patients, whereas another sentence talks about the drug, its dosage and the target group without mentioning the disease. Our methods cope well with such partially observed facts, at both pattern-learning and constraint-reasoning stages. 2018-04-28T01:06:34Z 2018-04-28 HighLife: Higher-arity Fact Harvesting talks/2018-04-12__Embed-Encode-Attend-Predict.pdf at master · explosion/talks · GitHub 2018-04-12 2018-04-12T23:39:42Z 2018-04-14 2018-04-14T11:41:25Z Research Blog: Introducing Semantic Experiences with Talk to Books and Semantris Andrew L. Beam Griffin Weber 2018-04-14 2018-04-04T16:02:54Z Inbar Fried Tianxi Cai Allen Schmaltz Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insights, we demonstrate how an insurance claims database of 60 million members, a collection of 20 million clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts. To evaluate our approach, we present a new benchmark methodology based on statistical power specifically designed to test embeddings of medical concepts. Our approach, called cui2vec, attains state-of-the-art performance relative to previous methods in most instances. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings Isaac S. Kohane [1804.01486] Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data Andrew L. Beam 2019-08-20T00:32:33Z Nathan P. Palmer Xu Shi 1804.01486 Benjamin Kompa 2018-04-14T11:10:40Z 2018-04-03T08:51:00Z 2018-04-03 How to Turn Your Website into a Mobile App with 7 Lines of JSON 2018-04-05 2018-04-05T02:09:47Z Context is Everything: Finding Meaning Statistically in Semantic Spaces (CS224n 2018) a new take on sentence embeddings Gartner fails spectacularly with its 180 degree flip on the impact of AI Automation on jobs - Horses for Sources 2018-04-09T21:59:08Z 2018-04-09 2018-04-12T23:46:27Z 2018-04-12 CNRS - Des fossiles dans les génomes pour dater l’arbre du vivant How to use Dataset in TensorFlow – Towards Data Science 2018-04-21T11:41:38Z 2018-04-21 Part-of-Speech tagging tutorial with the Keras Deep Learning library - Cdiscount TechBlog 2018-04-13T10:18:20Z 2018-04-13 2018-04-09T22:15:14Z 2018-04-09 Une plongée dans l’Afrique antique | CNRS Le journal Le site archéologique de Sedeinga, dans le nord du Soudan, offre un témoignage inédit des rites funéraires des royaumes de Napata et de Méroé qui régnèrent sur cette région du VIIe siècle avant notre ère jusqu’au IVe siècle Simon Gottschalk One of the key requirements to facilitate semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. EventKG presented in this paper is a multilingual event-centric temporal knowledge graph that addresses this gap. EventKG incorporates over 690 thousand contemporary and historical events and over 2.3 million temporal relations extracted from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical representation. 1804.04526 2018-04-12T14:12:48Z Simon Gottschalk EventKG: A Multilingual Event-Centric Temporal Knowledge Graph 2018-04-12T14:12:48Z 690 thousand contemporary and historical events and over 2.3 million temporal relations Elena Demidova [1804.04526] EventKG: A Multilingual Event-Centric Temporal Knowledge Graph 2018-04-15T08:43:10Z 2018-04-15 L’inventeur du Web exhorte à réguler l’intelligence artificielle 2018-04-28T16:16:19Z 2018-04-28 2018-04-14 2018-04-14T11:35:00Z Google Developers Blog: Text Embedding Models Contain Bias. Here's Why That Matters. GraphChain 2018-04-25T23:02:27Z 2018-04-25 Smart-MD: Neural Paragraph Retrieval of Medical Topics 2018-04-28T17:45:44Z 2018-04-28 2018-04-18 2018-04-18T10:00:40Z Open Semantic Search: Your own search engine for documents, images, tables, files, intranet & news 2018-04-26 2018-04-26T14:21:15Z Will capsule networks replace neural networks? - Quora Custom Similarity for ElasticSearch - Algorithms for Big Data 2018-04-03 2018-04-03T16:12:21Z Can you correct skew or perspective with … - Apple Community 2018-04-30T13:05:09Z 2018-04-30 2018-04-29 2018-04-29T13:16:48Z Who should hold the keys to our data? | Nigel Shadbolt and Roger Hampson | Opinion | The Guardian 2018-04-10 2018-04-10T17:45:54Z Fujitsu Ireland Research and Innovation | Knowledge Engineering and DIscovery (KEDI) Text Data Preprocessing: A Walkthrough in Python 2018-04-09T13:26:13Z 2018-04-09 2018-04-10T13:33:49Z Lessons Learned Reproducing a Deep Reinforcement Learning Paper 2018-04-10 GraphChain – A Distributed Database with Explicit Semantics and Chained RDF Graphs 2018-04-25T23:03:51Z 2018-04-25 2018-04-08 Two graph data models : RDF and Property Graphs 2018-04-08T12:45:59Z An overview of proxy-label approaches for semi-supervised learning 2018-04-26T14:15:55Z 2018-04-26 Going asynchronous: from Flask to Twisted Klein 2018-04-09T11:08:17Z 2018-04-09 Introduction to Twisted Klein, which is like Flask, but allows running asynchronous code. Text Classification with TensorFlow Estimators 2018-04-17T14:19:22Z 2018-04-17 2018-04-09T22:02:34Z 2018-04-09 Des civilisations oubliées peuplaient l'Amazonie au Moyen-Âge