PDF Artificial Intelligence. State of the Art Report

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History of artificial intelligence - Wikipedia

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Deep Learning State of the Art (2019) - MIT

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Artificial Intelligence (State of the art report)

Red fake Pyramid Scene Parsing Network. Natural Language Processing is one of the biggest collections of tasks on the site, with subsections on Machine Translation, Language Modelling, Sentiment Analysis, Text Classification and many others. One the most popular categories within this is Question Answering, with over papers on the subject. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.

Artificial Intelligence in Global Health: Defining a Collective Path Forward

Exploring the Limits of Language Modeling. Can Active Memory Replace Attention? Transfer learning is a methodology where weights from a model trained on one task are taken and used either:. This paper sets out to solve the problem that affects models using private data, in that a model may inadvertently and implicitly store some of its training data and that subsequent careful analysis of the model may therefore reveal sensitive information.

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To address this problem, the paper demonstrates a generally applicable approach to providing security for sensitive data: Private Aggregation of Teacher Ensembles PATE. The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users. This paper also includes a link to the GitHub repo with all the code in TensorFlow for this project. Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks. This work presents a compact, modular framework for constructing novel recurrent neural architectures.

Again, this paper also features a full working code example in TensorFlow.

How Fast Is AI Progressing? Stanford’s New Report Card for Artificial Intelligence

A recommendation system aim is to produce a list of recommendations for the user. This paper suggests a novel model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. The model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. Of course, we have just touched the surface on what Papers with code have to offer.