Marktechpost

NOVEMBER 14, 2023

The motivation for simplification arises from the complexity of modern neural network architectures and the gap between theory and practice in deep learning. The study conducted experiments on autoregressive decoder-only and BERT encoder-only models to assess the performance of the simplified transformers.

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AWS Machine Learning Blog

AUGUST 2, 2024

of nodes with text-features MAG 484,511,504 7,520,311,838 4/4 28,679,392 1,313,781,772 240,955,156 We benchmark two main LM-GNN methods in GraphStorm: pre-trained BERT+GNN, a baseline method that is widely adopted, and fine-tuned BERT+GNN, introduced by GraphStorm developers in 2022. Dataset Num. of nodes Num. of edges Num.

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ODSC - Open Data Science

APRIL 1, 2025

In a compelling talk at ODSC West 2024 , Yan Liu, PhD , a leading machine learning expert and professor at the University of Southern California (USC), shared her vision for how GPT-inspired architectures could revolutionize how we model, understand, and act on complex time series data acrossdomains. The result?

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BERT ML Engineer Data Science Deep Learning 52

Unite.AI

JUNE 21, 2024

The field of artificial intelligence (AI) has witnessed remarkable advancements in recent years, and at the heart of it lies the powerful combination of graphics processing units (GPUs) and parallel computing platform. Installation When setting AI development, using the latest drivers and libraries may not always be the best choice.

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Deep Learning Neural Network Convolutional Neural Networks Large Language Models 130

Unite.AI

OCTOBER 22, 2024

Understanding Computational Complexity in AI The performance of AI models depends heavily on computational complexity. In AI, particularly in deep learning , this often means dealing with a rapidly increasing number of computations as models grow in size and handle larger datasets.

Neural Network 147

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Marktechpost

APRIL 4, 2025

We’re using deepset/roberta-base-squad2 , which is: Based on RoBERTa architecture (a robustly optimized BERT approach) Fine-tuned on SQuAD 2.0 Let’s start by installing the necessary libraries: # Install required packages Copy Code Copied Use a different Browser !pip Windows NT 10.0; Windows NT 10.0; replace("[SEP]", "").strip()

AI Modeling 56

AI Modeling NLP AI AI 56

Marktechpost

JULY 5, 2024

The related works in this paper discuss a method called Scaling Laws for deep learning, which have become popular in recent years. Pre-trained embeddings like frozen ResNet-50 and BERT, are used to speed up training and prevent underfitting for CIFAR-10 and IMDB, respectively. high-quality data in AI research.

Machine Learning 130

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Marktechpost

NOVEMBER 10, 2024

BERT (Bidirectional Encoder Representations from Transformers) Google created the deep learning model known as BERT (Bidirectional Encoder Representations from Transformers) for natural language processing (NLP).

Python 87

Python Natural Language Processing BERT NLP 87

AssemblyAI

MAY 3, 2023

Effective methods allowing for better control, or steerability , of large-scale AI systems are currently in extremely high demand in the world of AI research. This process of adapting pre-trained models to new tasks or domains is an example of Transfer Learning , a fundamental concept in modern deep learning.

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NVIDIA

FEBRUARY 11, 2025

A Brief History of Foundation Models We are in a time where simple methods like neural networks are giving us an explosion of new capabilities, said Ashish Vaswani, an entrepreneur and former senior staff research scientist at Google Brain who led work on the seminal 2017 paper on transformers.

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Neural Network Large Language Models Robotics BERT 55

AWS Machine Learning Blog

SEPTEMBER 16, 2024

The backbone is a BERT architecture made up of 12 encoding layers. DNABERT 6 Dataset For this post, we use the gRNA data released by researchers in a paper about gRNA prediction using deep learning. Otherwise, the architecture of DNABERT is similar to that of BERT. CRISPRon is a CNN based deep learning model.

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Natural Language Processing Large Language Models BERT Neural Network 114

Marktechpost

JULY 4, 2023

Top Open Source Large Language Models GPT-Neo, GPT-J, and GPT-NeoX Extremely potent artificial intelligence models, such as GPT-Neo, GPT-J, and GPT-NeoX, can be used to Few-shot learning issues. Few-shot learning is similar to training and fine-tuning any deep learning model but requires fewer samples.

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Marktechpost

JULY 5, 2023

This GPT transformer architecture-based model imitates humans by answering questions accurately just like a human, generates content for blogs, social media, research, etc., Large Language Models like GPT, BERT, PaLM, and LLaMa have successfully contributed to the advancement in the field of Artificial Intelligence.

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Large Language Models Natural Language Processing Artificial Intelligence Artificial Intelligence 109

Topbots

APRIL 11, 2023

We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers.

NLP 98

NLP BERT Large Language Models Natural Language Processing 98

John Snow Labs

JUNE 27, 2023

In this section, we will provide an overview of two widely recognized LLMs, BERT and GPT, and introduce other notable models like T5, Pythia, Dolly, Bloom, Falcon, StarCoder, Orca, LLAMA, and Vicuna. BERT excels in understanding context and generating contextually relevant representations for a given text.

Large Language Models 52

Large Language Models BERT Natural Language Processing NLP 52

Marktechpost

JULY 3, 2023

With the release of the latest chatbot developed by OpenAI called ChatGPT, the field of AI has taken over the world as ChatGPT, due to its GPT’s transformer architecture, is always in the headlines. Almost every industry is utilizing the potential of AI and revolutionizing itself.

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Marktechpost

JULY 24, 2023

Researchers believe they can train a language model because of the competition to construct enormously large models that the power of scale has sparked. The initial BERT model is used for many real-world applications in natural language processing. However, this model already needed a substantial amount of computing to train.

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Marktechpost

JANUARY 4, 2024

The development of Large Language Models (LLMs), such as GPT and BERT, represents a remarkable leap in computational linguistics. The post Alibaba Researchers Unveil Unicron: An AI System Designed for Efficient Self-Healing in Large-Scale Language Model Training appeared first on MarkTechPost.

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Computational Linguistics Large Language Models LLM BERT 120

Flipboard

JULY 10, 2023

These are advanced machine learning models that are trained to comprehend massive volumes of text data and generate natural language. Examples of LLMs include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

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Marktechpost

JULY 19, 2023

Examples of text-only LLMs include GPT-3 , BERT , RoBERTa , etc. Why is there a need for Multimodal Language Models The text-only LLMs like GPT-3 and BERT have a wide range of applications, such as writing articles, composing emails, and coding. However, this text-only approach has also highlighted the limitations of these models.

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Artificial Intelligence Artificial Intelligence Large Language Models Robotics 98

Marktechpost

NOVEMBER 17, 2023

A transformer model with an additional scalar output for each token that can be utilized as a value function in reinforcement learning is presented in AutoModelForCausalLMWithValueHead and AutoModelForSeq2SeqLMWithValueHead. All Credit For This Research Goes To the Researchers on This Project. How does TRL work?

Machine Learning 127

Machine Learning Python BERT ML 127

Heartbeat

OCTOBER 10, 2023

RoBERTa RoBERTa (Robustly Optimized BERT Approach) is a natural language processing (NLP) model based on the BERT (Bidirectional Encoder Representations from Transformers) architecture. It was developed by Facebook AI Research and released in 2019. It is a state-of-the-art model for a variety of NLP tasks.

BERT 52

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ODSC - Open Data Science

APRIL 11, 2023

Speaker: Akash Tandon, Co-Founder and Co-author of Advanced Analytics with PySpark | Looppanel and O’Reilly Media Self-Supervised and Unsupervised Learning for Conversational AI and NLP Self-supervised and Unsupervised learning techniques such as Few-shot and Zero-shot learning are changing the shape of AI research and product community.

NLP 96

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AWS Machine Learning Blog

APRIL 29, 2024

Now, with today’s announcement, you have another straightforward compute option for workflows that need to train or fine-tune demanding deep learning models: running them on Trainium. He has worked as a researcher in academia, in customer-facing and engineering roles at MLOps startups, and as a product manager at Intel.

ML 120

ML Python Data Scientist Machine Learning 120

Marktechpost

JULY 16, 2023

Because of this, academics worldwide are looking at the potential benefits deep learning and large language models (LLMs) might bring to audio generation. In the last few weeks alone, four new papers have been published, each introducing a potentially useful audio model that can make further research in this area much easier.

Large Language Models 98

Large Language Models Categorization Natural Language Processing BERT 98

Marktechpost

NOVEMBER 3, 2023

A transformer model with an additional scalar output for each token that can be utilized as a value function in reinforcement learning is presented in AutoModelForCausalLMWithValueHead and AutoModelForSeq2SeqLMWithValueHead. All Credit For This Research Goes To the Researchers on This Project. How does TRL work?

Machine Learning 109

Machine Learning Python BERT ML 109

Topbots

OCTOBER 4, 2023

The predictive AI algorithms can be used to predict a wide range of variables, including continuous variables (e.g., They can be based on basic machine learning models like linear regression, logistic regression, decision trees, and random forests. Sign up for more AI research updates. whether a customer will churn).

Generative AI 119

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 119

Mlearning.ai

JUNE 8, 2023

A few embeddings for different data type For text data, models such as Word2Vec , GLoVE , and BERT transform words, sentences, or paragraphs into vector embeddings. However, it was not designed for transfer learning and needs to be trained for specific tasks using a separate model. What are Vector Embeddings?

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. Initially introduced for Natural Language Processing (NLP) applications like translation, this type of network was used in both Google’s BERT and OpenAI’s GPT-2 and GPT-3. But at what cost?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 52

ODSC - Open Data Science

FEBRUARY 14, 2023

For example, Seek AI , a developer of AI-powered intelligent data solutions, announced it has raised $7.5 Seek AI uses complex deep-learning foundation models with hundreds of billions of parameters. million in a combination of pre-seed and seed funding.

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AWS Machine Learning Blog

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. Gili Nachum is a senior AI/ML Specialist Solutions Architect who works as part of the EMEA Amazon Machine Learning team.

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Large Language Models LLM Machine Learning ML 96

Probably Approximately a Scientific Blog

JANUARY 12, 2021

In the last 5 years, popular media has made it seem that AI is nearly if not already solved by deep learning, with reports on super-human performance on speech recognition, image captioning, and object recognition. Credit for much of the content goes to the co-instructors, but any errors are mine. Using the AllenNLP demo.

Natural Language Processing 52

Natural Language Processing BERT NLP Neural Network 52

Viso.ai

DECEMBER 22, 2023

The Segment Anything Model (SAM), a recent innovation by Meta’s FAIR (Fundamental AI Research) lab, represents a pivotal shift in computer vision. This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. In this free live instance , the user can interactively segment objects and instances.

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Heartbeat

OCTOBER 12, 2023

From recognizing objects in images to discerning sentiment in audio clips, the amalgamation of language models with multi-modal learning opens doors to uncharted possibilities in AI research, development, and application in industries ranging from healthcare and entertainment to autonomous vehicles and beyond.

Large Language Models 59

Large Language Models Neural Network Convolutional Neural Networks Artificial Intelligence 59

Applied Data Science

DECEMBER 23, 2022

The first computational linguistics methods tried to bypass the immense complexity of human language learning by hard-coding syntax and grammar rules in their models. These early systems had difficulties learning long-range dependencies but were effective enough to be used in tasks such as translation. What happened?

Neural Network 52

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Marktechpost

JULY 11, 2023

Even OpenAI’s DALL-E and Google’s BERT have contributed to making significant advances in recent times. Recently, a new AI tool has been released, which has even more potential than ChatGPT. GPT 4, which is the latest add-on to OpenAI’s deep learning models, is multimodal in nature. What is AutoGPT?

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Topbots

JUNE 15, 2023

Are All Languages Created Equal in Multilingual BERT? In Proceedings of the 5th Workshop on Representation Learning for NLP , pages 120–130, Online. Sign up for more AI research updates. Email Address * Name * First Last Company * What areas of AI research are you interested in? Shijie Wu and Mark Dredze.

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Natural Language Processing Computational Linguistics NLP ChatGPT 59