Marek Rei

NOVEMBER 12, 2019

Evaluations on CoNLL 2014 and JFLEG show a considerable improvement over previous best results of neural models, making this work comparable to state-of-the art on error correction. link] Constructing a system for NLI that explains its decisions by pointing to the most relevant parts of the input. Cambridge, Amazon. NAACL 2019.

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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). These ideas also move in step with the explainability of results.

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Explosion

FEBRUARY 18, 2015

I wrote it because I think small companies are terrible at natural language processing (NLP). Or rather: small companies are using terrible NLP technology. To do great NLP, you have to know a little about linguistics, a lot about machine learning, and almost everything about the latest research. Amazing improvements in quality.

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NLP Natural Language Processing Software Engineer Explainability 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.

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. NLP skills have long been essential for dealing with textual data. Tokenization & Transformers These are specific techniques in NLP and popularized by LLMs. Tokenization involves converting text into a format understandable by models.

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Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? sentence embeddings: represent entire sentences.

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Sebastian Ruder

MAY 22, 2020

This advice should be most relevant to people studying machine learning (ML) and natural language processing (NLP) as that is what I did in my PhD. 2014 ), neuroscience ( Wang et al., Many projects with a large impact in ML and NLP such as AlphaGo or OpenAI Five have been developed by a team. 2016 ), physics ( Cohen et al.,

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Pragnakalp

JANUARY 1, 2024

There are several theories and hypotheses that attempt to explain what might have come before the Big Bang, but none of them have been proven conclusively. Overall, while there are many theories and hypotheses that attempt to explain what came before the Big Bang, none of them have been proven conclusively. Mistral-7b-instruct-v0.1

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John Snow Labs

AUGUST 7, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). Healthcare NLP (Natural Language Processing) technologies extract insights from physician records, patient histories and diagnostic reports facilitating precise diagnosis.

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John Snow Labs

FEBRUARY 29, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). Healthcare NLP (Natural Language Processing) technologies extract insights from physician records, patient histories and diagnostic reports facilitating precise diagnosis.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading!

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

Probably Approximately a Scientific Blog

MARCH 1, 2017

It seems that the problem lies in occupational gender segregation , which may be explained by any one of the following: 2.2 It seems that the problem lies in occupational gender segregation , which may be explained by any one of the following: 2.2 From [link] According to the Central Bureau of Statistics , in 2014, 48.9%

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Explainability NLP Machine Learning 52

Unite.AI

DECEMBER 15, 2023

I’ve spent most of the last 5 years focused on NLP use cases and problems. For example, for NLP tasks, they can help by providing alternative ways of phrasing problematic snippets of text such that the bias present in the language is reduced. Thus, building good explainability and interpretability tools is important.

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Ehud Reiter

DECEMBER 15, 2024

I also loved their example of domain drift, where GPT gave a wrong answer because it used an old law which had been superceded in 2014 (ie GPT could not deal with a domain change that happened 10 years ago). But the overall message is similar to our work (eg, Thomson et al (2024)); experimental quality in NLP is often poor.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

text generation model on domain-specific datasets, enabling it to generate relevant text and tackle various natural language processing (NLP) tasks within a particular domain using few-shot prompting. Instruction tuning format In instruction fine-tuning, the model is fine-tuned for a set of NLP tasks described using instructions.

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