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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

NLP 98
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The Evolution of the GPT Series: A Deep Dive into Technical Insights and Performance Metrics From GPT-1 to GPT-4o

Marktechpost

The Generative Pre-trained Transformer (GPT) series, developed by OpenAI, has revolutionized the field of NLP with its groundbreaking advancements in language generation and understanding. GPT-1: The Beginning Launched in June 2018, GPT-1 marked the inception of the GPT series. Model Size: 1.5

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ChatGPT & Advanced Prompt Engineering: Driving the AI Evolution

Unite.AI

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

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ML and NLP Research Highlights of 2020

Sebastian Ruder

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). 2018 ; Howard et al.,  2020 saw the development of ever larger language and dialogue models such as Meena ( Adiwardana et al.,

NLP 52
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74 Summaries of Machine Learning and NLP Research

Marek Rei

ArXiv 2018. The generative part is then evaluated as a language model, while the inference network is evaluated as an unsupervised unlabeled constituency parser. EMNLP 2018. NAACL 2018. NAACL 2018. At the end, I also include the summaries for my own published papers since the last iteration (papers 61-74).

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Vision Transformers (ViT) in Image Recognition – 2023 Guide

Viso.ai

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

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Dude, Where’s My Neural Net? An Informal and Slightly Personal History

Lexalytics

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.