Marktechpost

NOVEMBER 10, 2023

AugGPT’s framework consists of fine-tuning BERT on the base dataset, generating augmented data (Daugn) using ChatGPT, and fine-tuning BERT with the augmented data. The few-shot text classification model is based on BERT, using cross-entropy and contrastive loss functions to classify samples effectively.

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Mlearning.ai

MARCH 2, 2023

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. 1 Architecture III.2

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Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Source: A pipeline on Generative AI This figure of a generative AI pipeline illustrates the applicability of models such as BERT, GPT, and OPT in data extraction.

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Viso.ai

MARCH 26, 2024

This drastically enhanced the capabilities of computer vision systems to recognize patterns far beyond the capability of humans. In this article, we present 7 key applications of computer vision in finance: No.1: Applications of Computer Vision in Finance No. 1: Fraud Detection and Prevention No.2:

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Flipboard

DECEMBER 6, 2023

In this post, we focus on the BERT extractive summarizer. BERT extractive summarizer The BERT extractive summarizer is a type of extractive summarization model that uses the BERT language model to extract the most important sentences from a text. It works by first embedding the sentences in the text using BERT.

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Unite.AI

JUNE 5, 2024

Notably, Google's implementation of pruning on BERT resulted in a substantial 30—40% reduction in size with minimal accuracy compromise, thereby facilitating swifter deployment. For example, in computer vision, adaptive methods enable efficient processing of high-resolution images while accurately detecting objects.

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Pickl AI

APRIL 6, 2025

Use Cases: Image recognition, object detection, image segmentation, computer vision tasks, medical image analysis, can also be adapted for NLP (text classification). BERT) and decoder-only (e.g., Fully Connected Layers: Often used at the end to perform classification based on the extracted features.

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Towards AI

JANUARY 7, 2025

Introduction The idea behind using fine-tuning in Natural Language Processing (NLP) was borrowed from Computer Vision (CV). In the case of BERT (Bidirectional Encoder Representations from Transformers), learning involves predicting randomly masked words (bidirectional) and sentence-order prediction.

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Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Image captioning integrates computer vision, which interprets visual information, and NLP, which produces human language.

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IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy. An open-source model, Google created BERT in 2018.

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Unite.AI

NOVEMBER 6, 2023

For instance, NN used for computer vision tasks (object detection and image segmentation) are called convolutional neural networks (CNNs) , such as AlexNet , ResNet , and YOLO. Prominent transformer models include BERT , GPT-4 , and T5. Do We Still Need Traditional Machine Learning Algorithms?

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Unite.AI

APRIL 2, 2024

These problems, commonly referred to as degradations in low-level computer vision, can arise from difficult environmental conditions like heat or rain or from limitations of the camera itself. An image can convey a great deal, yet it may also be marred by various issues such as motion blur, haze, noise, and low dynamic range.

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Marktechpost

MARCH 20, 2025

Case studies from five cities demonstrate reductions in carbon emissions and improvements in quality of life metrics." }, { "id": 6, "title": "Neural Networks for Computer Vision", "abstract": "Convolutional neural networks have revolutionized computer vision tasks.

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Marktechpost

SEPTEMBER 17, 2023

The creation of transformer-based NLP models has sparked advancements in designing and using transformer-based models in computer vision and other modalities. Large language models (LLMs) built on transformers, including ChatGPT and GPT-4, have demonstrated amazing natural language processing abilities.

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NVIDIA

SEPTEMBER 11, 2023

Grace Hopper Superchips and H100 GPUs led across all MLPerf’s data center tests, including inference for computer vision, speech recognition and medical imaging, in addition to the more demanding use cases of recommendation systems and the large language models ( LLMs ) used in generative AI.

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DataRobot Blog

APRIL 21, 2022

The natural follow-up question is if this increase in computing requirements has led to an increase in accuracy. The below graph illustrates accuracy versus model size for some of the more well-known computer vision models. Some of the models offer a slight improvement in accuracy but at an immense cost of computer resources.

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Flipboard

JUNE 20, 2023

Training experiment: Training BERT Large from scratch Training, as opposed to inference, is a finite process that is repeated much less frequently. Training a well-performing BERT Large model from scratch typically requires 450 million sequences to be processed. The first uses traditional accelerated EC2 instances.

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Marktechpost

APRIL 20, 2024

Deep neural networks like convolutional neural networks (CNNs) have revolutionized various computer vision tasks, from image classification to object detection and segmentation. As models grew larger and more complex, their accuracy soared. Check out the Paper. All credit for this research goes to the researchers of this project.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

This satisfies the strong MME demand for deep neural network (DNN) models that benefit from accelerated compute with GPUs. These include computer vision (CV), natural language processing (NLP), and generative AI models. We tested two NLP models: bert-base-uncased (109M) and roberta-large (335M).

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Unite.AI

OCTOBER 22, 2024

Put simply, if we double the input size, the computational needs can increase fourfold. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

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Marktechpost

JUNE 7, 2023

The transformer models like BERT and T5 have recently got popular due to their excellent properties and have utilized the idea of self-supervision in Natural Language Processing tasks. Self-supervised learning is being prominently used in Artificial Intelligence to develop intelligent systems.

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Towards AI

JULY 19, 2023

Big Data for Justice An open-access dataset of 80 million Indian legal case records devdatalab.medium.com OCR Library | Azure Azure bringing their new version of OCR to their computer vision library it includes: OCR for 73 languages including Simplified and Traditional Chinese, Japanese, Korean, and several Latin languages.

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Towards AI

JANUARY 14, 2025

Understanding Vision Transformers (ViTs) And what I learned while implementing them! Transformers have revolutionized natural language processing (NLP), powering models like GPT and BERT. But recently, theyve also been making waves in computer vision.

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Unite.AI

SEPTEMBER 18, 2023

Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more. NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT.

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Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks.

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Pickl AI

JANUARY 26, 2025

Applications in Computer Vision CNNs dominate computer vision tasks such as object detection, image classification, and facial recognition. Transformers are the foundation of many state-of-the-art architectures, such as BERT and GPT.

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AWS Machine Learning Blog

JUNE 18, 2024

Traditional neural network models like RNNs and LSTMs and more modern transformer-based models like BERT for NER require costly fine-tuning on labeled data for every custom entity type. Her expertise is in building machine learning solutions involving computer vision and natural language processing for various industry verticals.

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Marktechpost

NOVEMBER 20, 2023

Recently, convolutions have emerged as a critical primitive for sequence modeling, supporting state-of-the-art performance in language modeling, time-series analysis, computer vision, DNA modeling, and more. points better perplexity and allows M2-BERT-base to achieve up to 3.3 and by up to 5.60

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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.

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Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc. Applications in Computer Vision Models like ResNET, VGG, Image Captioning, etc. Foundation models are recent developments in artificial intelligence (AI).

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AWS Machine Learning Blog

MARCH 20, 2023

Quantization is a technique to reduce the computational and memory costs of running inference by representing the weights and activations with low-precision data types like 8-bit integer (INT8) instead of the usual 32-bit floating point (FP32). In the following example figure, we show INT8 inference performance in C6i for a BERT-base model.

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Towards AI

AUGUST 24, 2023

While domains such as language and computer vision still dominate the headlines, speech is becoming an increasingly important domain. Meta AI employs a self-supervised speech encoder known as w2v-BERT 2.0 — an enhanced iteration of w2v-BERT distinguished by improved training stability and representation quality.

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Marktechpost

JUNE 4, 2024

Table 1 compares the average time per training or inference step for models like SAM, Gemma, BERT, and Mistral across different versions and frameworks of Keras. KerasCV and KerasNLP publish all pretrained models on Kaggle Models, which are accessible in Kaggle competition notebooks even in Internet-off mode.

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Towards AI

DECEMBER 28, 2023

The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. The introduction of attention mechanisms has notably altered our approach to working with deep learning algorithms, leading to a revolution in the realms of computer vision and natural language processing (NLP).

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AWS Machine Learning Blog

APRIL 7, 2023

As an example, smart venue solutions can use near-real-time computer vision for crowd analytics over 5G networks, all while minimizing investment in on-premises hardware networking equipment. In our example, we use the Bidirectional Encoder Representations from Transformers (BERT) model, commonly used for natural language processing.

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PyImageSearch

AUGUST 14, 2023

As shown in Figure 10 , the module uses a BERT (bidirectional encoder representations from transformers) model, which performs classification on top of classification token ([CLS]) output embedding. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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Unite.AI

SEPTEMBER 9, 2024

Similarly, computer vision methods are progressively embracing extensive data scales for pretraining. AIM explores the scalability of autoregressive visual pretraining similar to BERT for vision transformers. The pursuit of large-scale 3D human digitization remains a pivotal goal in computer vision.

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