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MobileBERT: BERT for Resource-Limited Devices

Analytics Vidhya

The post MobileBERT: BERT for Resource-Limited Devices appeared first on Analytics Vidhya. Overview As the size of the NLP model increases into the hundreds of billions of parameters, so does the importance of being able to.

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UltraFastBERT: Exponentially Faster Language Modeling

Unite.AI

This article introduces UltraFastBERT, a BERT-based framework matching the efficacy of leading BERT models but using just 0.3% of the available neurons while delivering results comparable to BERT models with a similar size and training process, especially on the downstream tasks.

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Do LLMs Remember Like Humans? Exploring the Parallels and Differences

Unite.AI

In contrast, LLMs rely on static data patterns and mathematical algorithms. LLMs, such as GPT-4 and BERT , operate on entirely different principles when processing and storing information. However, despite these abilities, how LLMs store and retrieve information differs significantly from human memory. How Human Memory Works?

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Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

AWS Machine Learning Blog

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. First, we use an Amazon SageMaker Studio notebook to fine-tune a pre-trained BERT model on a target task using a domain-specific dataset.

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How to Become a Generative AI Engineer in 2025?

Towards AI

GPT, BERT) Image Generation (e.g., These are essential for understanding machine learning algorithms. Explore text generation models like GPT and BERT. Hugging Face: For working with pre-trained NLP models like GPT and BERT. Generative AI Techniques: Text Generation (e.g., GANs, DALLE) Music and Video Generation 5.

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Generative AI versus Predictive AI

Marktechpost

One of the earliest and widely recognized works in predictive modeling within deep learning is the Recurrent Neural Network (RNN) based language model by Tomas Mikolov , which demonstrated how predictive algorithms could capture sequential dependencies to predict future tokens in language tasks.

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Overcoming Gradient Inversion Challenges in Federated Learning: The DAGER Algorithm for Exact Text Reconstruction

Marktechpost

have developed DAGER, an algorithm that precisely recovers entire batches of input text. DAGER outperforms previous attacks in speed, scalability, and reconstruction quality, recovering batches up to size 128 on large language models like GPT-2, LLaMa-2, and BERT. Researchers from INSAIT, Sofia University, ETH Zurich, and LogicStar.ai

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