Sebastian Ruder

JANUARY 6, 2020

This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019. 2019 ) and other variants. In biology, Transformer language models have been pretrained on protein sequences ( Rives et al., 2019 ), MoCo ( He et al., 2019 ), MoCo ( He et al., 2019 ) and domains ( Desai et al.,

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. While even knowing one of these is attractive, being flexible and adaptable by knowing all three and more will really pop.

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Sebastian Ruder

AUGUST 18, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. 2017 ) and pretrained language models ( Peters et al., 2019 ) of recent years. A taxonomy that highlights the variations can be seen below: A taxonomy for transfer learning in NLP ( Ruder, 2019 ). 2019 ; Artetxe and Schwenk, 2019 ; Mulcaire et al.,

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Unite.AI

OCTOBER 27, 2023

For example, multimodal generative models of neural networks can produce such images, literary and scientific texts that it is not always possible to distinguish whether they are created by a human or an artificial intelligence system.

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Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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Flipboard

JULY 10, 2023

An autoencoder is an artificial neural network used for unsupervised learning tasks (i.e., Sequence-to-Sequence Autoencoder Also known as a Recurrent Autoencoder, this type of autoencoder utilizes recurrent neural network (RNN) layers (e.g., time series or natural language processing tasks).

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Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

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Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis.

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Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

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Viso.ai

DECEMBER 20, 2023

MindSpore Open Source AI Framework for Audio, NLP, and Vision – Source: workshop Mindspore Architecture: Understanding Its Core Huawei’s MindSpore contains a modular and efficient architecture for neural network training and inference. Simply put, it makes complex neural network computations more efficient and effective.

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Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). NAACL 2019. link] A bidirectional transformer architecture for pre-training language representations. ArXiv 2019.

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Viso.ai

FEBRUARY 23, 2024

Prompt-based Segmentation combines the power of natural language processing (NLP) and computer vision to create an image segmentation model. OMG-Seg explores co-training on various datasets, including COCO panoptic, COCO-SAM, and Youtube-VIS-2019. One such development is prompt-based segmentation.

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Sebastian Ruder

OCTOBER 26, 2019

This post expands on the ACL 2019 tutorial on Unsupervised Cross-lingual Representation Learning. The domains in this case are different languages. 2017 ; Nicolai & Yarowsky, 2019 ), distant supervision ( Plank & Agić, 2018 ) or machine translation (MT; Zhou et al., 2016 ; Eger et al., Why not Machine Translation?

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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). 2019 ) and work that focuses on making them smaller has gained momentum: Recent approaches rely on pruning ( Sajjad et al.,

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Heartbeat

JANUARY 29, 2024

Imagine an AI system that becomes proficient in many tasks through extensive training on each specific problem and a higher-order learning process that distills valuable insights from previous learning endeavors. NTMs introduced the revolutionary concept of incorporating dynamic memory storage into neural networks.

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Bugra Akyildiz

JULY 16, 2023

Google created a new learning path guides you through a curated collection of content on generative AI products and technologies, from the fundamentals of Large Language Models to how to create and deploy generative AI solutions on Google Cloud. HuggingFace has a new class called Audio where they talk about Text to Speech (TTS).

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Viso.ai

JUNE 12, 2024

GPT models are based on transformer-based deep learning neural network architecture. Their applications include various Natural Language Processing ( NLP ) tasks, including question answering, text summarization, sentiment analysis , etc. All three GPT generations utilize artificial neural networks.

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Viso.ai

JULY 9, 2024

Neural Networks For now, most attempts to develop ASI are still grounded in well-known models, such as neural networks , machine learning/deep learning , and computational neuroscience. Estimates for when we would achieve AGI range from the next 5 to 30 years. It contains 1.15

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Advances in neural information processing systems 32 (2019). His research interests are graph neural networks, computer vision, time series analysis and their industrial applications. He is broadly interested in Deep Learning and Natural Language Processing. The Illustrated Transformer.”

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Explosion

NOVEMBER 21, 2019

Try the new interactive demo to explore similarities and compare them between 2015 and 2019 sense2vec (Trask et. First, we trained a new sense2vec model on the 2019 Reddit comments , which makes for an interesting contrast to the previous 2015 vectors. In 2019, it’s mostly used in the context of cutting off communication by “ghosting”.

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Snorkel AI

MAY 25, 2023

Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment. The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment.

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Snorkel AI

MAY 25, 2023

Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment. The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment.

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Mlearning.ai

MARCH 9, 2023

2019) proposed a novel adversarial training framework for improving the robustness of deep learning-based segmentation models. 2019) or by using input pre-processing techniques to remove adversarial perturbations (Xie et al., Neural Networks, 64, 59–63. Intriguing properties of neural networks.

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Heartbeat

OCTOBER 10, 2023

One of the most popular techniques for speech recognition is natural language processing (NLP), which entails training machine learning models on enormous amounts of text data to understand linguistic patterns and structures. It was developed by Facebook AI Research and released in 2019. Why Did RoBERTa Get Developed?

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Heartbeat

MARCH 15, 2023

Photo by Fatos Bytyqi on Unsplash Introduction Did you know that in the past, computers struggled to understand human languages? But now, a computer can be taught to comprehend and process human language through Natural Language Processing (NLP), which was implemented, to make computers capable of understanding spoken and written language.

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Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. Just wait until you hear what happened in 2022. Who should I follow?

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John Snow Labs

JUNE 6, 2023

Sequence classification with transformers is a powerful technique in the field of natural language processing (NLP) that involves predicting a label or category for a sequence of data. It is a smaller and more efficient variation of BERT that was introduced by Google AI in 2019. setInputCol('text').setOutputCol('document')

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AWS Machine Learning Blog

MARCH 28, 2024

This architecture allows different parts of a neural network to specialize in different tasks, effectively dividing the workload among multiple experts. One of the key components of SageMaker JumpStart is the Model Hub, which offers a vast catalog of pre-trained models, such as the Mixtral-8x7B, for a variety of tasks.

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The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. We will now see how LinkBERT performs on several downstream natural language processing tasks. Let’s use LinkBERT! Brown, et al.

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Explosion

AUGUST 1, 2019

Transformers and transfer-learning Natural Language Processing (NLP) systems face a problem known as the “knowledge acquisition bottleneck”. Deep neural networks have offered a solution, by building dense representations that transfer well between tasks. We have updated our library and this blog post accordingly.

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The MLOps Blog

APRIL 5, 2023

For example, Modularizing a natural language processing (NLP) model for sentiment analysis can include separating the word embedding layer and the RNN layer into separate modules, which can be packaged and reused in other NLP models to manage code and reduce duplication and computational resources required to run the model.

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Heartbeat

JUNE 28, 2023

ALBERT (A Lite BERT) is a language model developed by Google Research in 2019. Image from: [link] Overall, knowledge distillation is a powerful technique that can be used to reduce the size and complexity of deep neural networks, while maintaining or even improving their performance on a given task.

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PyImageSearch

JANUARY 2, 2023

Figure 7: AI in Dermatology (source: Liu and Bui, 2019 ). Because of its sensitive nature, managing mental health is more effective when the person receiving care interacts with the healthcare provider. Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools.

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Bugra Akyildiz

NOVEMBER 17, 2024

Vision Transformers(ViT) ViT is a type of machine learning model that applies the transformer architecture, originally developed for natural language processing, to image recognition tasks. 2019; Lou, et al. and 8B base and chat models, supporting both English and Chinese languages.

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Mlearning.ai

JUNE 28, 2023

I came up with an idea of a Natural Language Processing (NLP) AI program that can generate exam questions and choices about Named Entity Recognition (who, what, where, when, why). See the attachment below. A Named Entity Recognition question example from OpExams — Free question generator. The approach was proposed by Yin et al.

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Explosion

MARCH 17, 2019

of the spaCy Natural Language Processing library includes a huge number of features, improvements and bug fixes. spaCy is an open-source library for industrial-strength natural language processing in Python. when we switched over to neural network models. Version 2.1 All that changed in v2.0,

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In this example figure, features are extracted from raw historical data, which are then are fed into a neural network (NN). The Inferentia chip became generally available (GA) in December 2019, followed by Trainium GA in October 2022, and Inferentia2 GA in April 2023. PBAs, such as GPUs, can be used for both these steps.

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