Heartbeat

DECEMBER 20, 2023

This blog aims to equip you with a thorough understanding of these powerful neural network architectures. Whether you’re a seasoned AI researcher or a budding enthusiast in machine learning, the insights offered here will deepen your understanding and guide you in leveraging the full potential of CNNs in various applications.

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AI Weekly

AUGUST 17, 2023

Liu Yang from the University of Chinese Academy of Sciences (UCAS), in collaboration with her colleagues from Renmin University of China and Massachusetts Institute of Technology, has proposed a novel network, namely, the physics-encoded recurrent convolutional neural network (PeRCNN). You can also subscribe via email.

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AssemblyAI

JANUARY 23, 2025

Unlike many natural language processing (NLP) models, which were historically dominated by recurrent neural networks (RNNs) and, more recently, transformers, wav2letter is designed entirely using convolutional neural networks (CNNs). Despite this, it remains widely recognized by its original name, wav2letter.

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AI Weekly

MARCH 21, 2024

By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI. Join the AI conversation and transform your advertising strategy with AI weekly sponsorship This RSS feed is published on [link].

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Marktechpost

NOVEMBER 21, 2023

A new AI research presents DeepColony , a comprehensive framework for colony identification and analysis in microbiology laboratories. The system’s architecture involves convolutional neural networks (CNNs) organized in a hierarchical structure.

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AI Weekly

DECEMBER 7, 2023

Connect with 5,000+ attendees including industry leaders, heads of state, entrepreneurs and researchers to explore the next wave of transformative AI technologies. theconversation.com Who will win the battle for AI in the cloud? Our findings revealed that the DCNN, enhanced by this specialised training, could surpass.

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Marktechpost

JULY 21, 2023

Analogous to the human brain’s visual cortex; V1, V2, V3, and IPS are visual processing streams in the Deep neural network. With deep neural networks at both the single unit and distributed population levels, neural coding of quantity emergence with learning can be investigated. appeared first on MarkTechPost.

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Marktechpost

SEPTEMBER 4, 2023

Recent advancements in deep neural networks have enabled new approaches to address anatomical segmentation. For instance, state-of-the-art performance in the anatomical segmentation of biomedical images has been attained by deep convolutional neural networks (CNNs).

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Marktechpost

JULY 14, 2023

This image representation comes under a broad category of Computer Vision and Convolutional Neural Networks. Researchers developed a Composed image retrieval (CIR) system to have a minimal loss, but the problem with this method was that it requires a large dataset for training the model.

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Marktechpost

JULY 21, 2023

In Study 1, researchers used the facial emotion detection method to analyze the emotional features from the recorded video frames. The Multi-Task Convolutional Neural Network (MTCNN) and VGG19 neural network were used for facial detection and emotional recognition, respectively.

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Marktechpost

AUGUST 25, 2023

A team of researchers from Apple and the University of California, Santa Barbara created a direct inference of scene-level 3D geometry using deep neural networks, which didn’t involve the traditional method of test-time optimization. All Credit For This Research Goes To the Researchers on This Project.

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Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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AWS Machine Learning Blog

MARCH 21, 2025

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Marktechpost

NOVEMBER 16, 2023

A new research paper presents a deep learning-based classifier for age-related macular degeneration (AMD) stages using retinal optical coherence tomography (OCT) scans. The two-stage convolutional neural network accurately classified macula-centered 3D volumes into four classes: Normal, iAMD, GA, and nAMD.

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Marktechpost

OCTOBER 11, 2023

Studies now investigate if building AI research agents with similar capabilities is possible. To evaluate AI research agents with free-form decision-making capabilities, researchers from Stanford University propose MLAgentBench, the first benchmark of its kind. Join our AI Channel on Whatsapp.

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Marktechpost

JUNE 15, 2023

Convolutional neural networks can use Images with translational symmetry, and permutation symmetry in graphs can be used by graph neural networks. Theoretical research and practical methods for constructing general group equivariant neural networks have seen a recent uptick in interest.

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Unite.AI

OCTOBER 22, 2024

We use Big O notation to describe this growth, and quadratic complexity O(n²) is a common challenge in many AI tasks. 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

SEPTEMBER 12, 2023

It leverages the capabilities of deep learning models, such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). All Credit For This Research Goes To the Researchers on This Project. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 21, 2023

Their pioneering breakthrough, named Senseiver, showcases a neural network that achieves a remarkable feat: representing extensive data with minimal computational resources. The team developed a neural network that allows them to represent a large system in a very compact way.

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Marktechpost

DECEMBER 29, 2023

To overcome these obstacles and raise the precision and adaptability of awn analysis, the research team suggests enlarging the training set and investigating different convolutional neural network (CNN) models. Researchers used binary cross-entropy loss and Dice Coefficient (DC) for training and validating the model.

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Marktechpost

DECEMBER 15, 2023

This method involves the application of a generative neural network, specifically a Generative Adversarial Network (GAN), a form of AI. According to researchers, this is the reason for developing GAN, an AI-based generative neural network trained using high-resolution radar precipitation fields.

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Marktechpost

JUNE 30, 2023

The first step depends on using a detector based on a Convolutional Neural Network (CNN). They show that a real-time model for any arbitrary data segment is feasible using the computational efficiency of convolutional neural networks (CNNs). Segmentation masks for each instance in the image are generated.

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Marktechpost

OCTOBER 10, 2023

Its applications are used in many fields, such as image and speech recognition for language processing, object detection, and medical imaging diagnostics; finance for algorithmic trading and fraud detection; autonomous vehicles using convolutional neural networks for real-time decision-making; and recommendation systems for personalized content.

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Marktechpost

OCTOBER 21, 2023

These models were the basis for the generative AI tools mentioned above and were trained on an enormous cloud of powerful graphics processing units (GPUs). All Credit For This Research Goes To the Researchers on This Project. Join our AI Channel on Whatsapp. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 30, 2023

The research outlines the development of a machine learning-based binary classifier capable of detecting an elusive icosahedral quasicrystal (i-QC) phase from multiphase powder X-ray diffraction patterns. The researchers constructed a binary classifier employing 80 convolutional neural networks.

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Marktechpost

DECEMBER 24, 2023

Deep Learning: This branch of machine learning involves convolutional neural networks (CNN), which have gained prominence due to their ability to extract features from images automatically, reducing the reliance on manual feature engineering. All credit for this research goes to the researchers of this project.

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Marktechpost

OCTOBER 1, 2023

This is because, whereas the size of the convolutional kernel constrains convolutional neural networks (CNNs) and can only extract local information, self-attention can remove global information from the picture, delivering adequate and meaningful visual characteristics. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 15, 2023

An adaptive softmax Convolutional Neural Network (CNN) kernel is utilized in the lower blocks, with its kernel size determined by the timestep and speaker. All credit for this research goes to the researchers of this project. If you like our work, you will love our newsletter. We are also on Telegram and WhatsApp.

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Marktechpost

JULY 16, 2023

The Vision Transformer (ViT) rapidly replaces convolution-based neural networks because of its simplicity, flexibility, and scalability. Feeding data into a deep neural network during training and operation in batches is common practice. All Credit For This Research Goes To the Researchers on This Project.

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Mlearning.ai

JULY 17, 2023

How taking inspiration from the brain can help us create Neural Networks. Data Scientists spend a lot of time and resources in improving the architecture of their AI Models. As such, there is a lot of research into figuring out different architectures and how different design decisions impact the performance of models.

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Marktechpost

SEPTEMBER 1, 2023

They found that the careful selection of algorithms significantly influences authentication performance, with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) emerging as leaders in handling physiological and behavioral dynamics. If you like our work, you will love our newsletter.

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Marktechpost

SEPTEMBER 17, 2023

In all cases, the recovery phase involves retraining a neural network model to undo the damage. Using the dilated convolutional neural network architecture first seen in the CARP protein masked language model, they trained all EvoDiff sequence models on 42M sequences from UniRef50.

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Marktechpost

DECEMBER 12, 2023

The FGS has been used to predict facial landmark placements and pain scores by utilizing deep neural networks and machine learning models. All credit for this research goes to the researchers of this project. If you like our work, you will love our newsletter.

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Marktechpost

DECEMBER 15, 2023

CNNs (Convolutional neural networks) have become a popular technique for image recognition in recent years. However, new challenges have emerged as these networks have grown more complex. All credit for this research goes to the researchers of this project. If you like our work, you will love our newsletter.

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Marktechpost

OCTOBER 30, 2023

Researchers have challenged the prevailing belief in the field of computer vision that Vision Transformers (ViTs) outperform Convolutional Neural Networks (ConvNets) when given access to large web-scale datasets. All Credit For This Research Goes To the Researchers on This Project.

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Marktechpost

SEPTEMBER 29, 2024

In response, Google utilizes a deep neural network, CTG-net, to process the time-series data of fetal heart rate (FHR) and uterine contractions (UC) in order to predict fetal hypoxia. The CTG-net model utilizes a convolutional neural network (CNN) architecture to analyze FHR and UC signals, learning their temporal relationships.

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Marktechpost

JUNE 24, 2023

They also evaluate the method against a state-of-the-art convolutional neural network (CNN) model used for forensic picture classification and find that their methods perform better. According to the team, their method can be easily compromised by a cropping attack, which is a major disadvantage.

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