Explosion

NOVEMBER 9, 2016

Most NLP problems can be reduced to machine learning problems that take one or more texts as input. However, most NLP problems require understanding of longer spans of text, not just individual words. This has always been a huge weakness of NLP models. 2016) presented a model that achieved 86.8% Now we have a solution.

Deep Learning 52

Deep Learning NLP Convolutional Neural Networks Neural Network 52

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

NLP 52

NLP ML Computer Vision Natural Language Processing 52

Snorkel AI

MARCH 1, 2023

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. 2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. Attention Is All You Need Vaswani et al.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). The complete set of generated words is the output sequence (or sentence) of the network.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. NLP is a particularly crucial element of the multi-discipline research problem that is VQA. is an object detection task.

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

Lexalytics

APRIL 5, 2021

I was out of the neural net biz. Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) Hinton (again!) and BERT.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. from_disk("/path/to/s2v_reddit_2015_md") nlp.add_pipe(s2v) doc = nlp("A sentence about natural language processing.") That work is now due for an update. assert doc[3:6].text

NLP 52

NLP Convolutional Neural Networks Neural Network Natural Language Processing 52