Analytics Vidhya

JUNE 15, 2022

Deep Learning is a subfield of Machine Learning, inspired by the biological neurons of a brain, and translated to artificial neural networks with representation learning. Dear Readers, We bring you another episode of our DataHour series. In this DataHour session, Umang will take you through a fun ride of live DEMO!

Neural Network 336

Neural Network Deep Learning Machine Learning 336

AI News

AUGUST 13, 2024

Enter SingularityNET’s ambitious plan: a “multi-level cognitive computing network” designed to host and train the incredibly complex AI architectures required for AGI. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Big Data 336

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Marktechpost

JULY 26, 2024

Sparsity in neural networks is one of the critical areas being investigated, as it offers a way to enhance the efficiency and manageability of these models. By focusing on sparsity, researchers aim to create neural networks that are both powerful and resource-efficient. Check out the Paper.

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Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. One of the central challenges in this field is the extended time needed to train complex neural networks.

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Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 111

AI News

DECEMBER 18, 2023

A key challenge she encounters is misunderstandings around what AI truly means – many conflate it solely with chatbots like ChatGPT rather than appreciating the full breadth of machine learning, neural networks, natural language processing, and more that enable today’s AI. “There’s a lot of misconceptions, definitely.

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AI News

MAY 13, 2024

This technological foundation enables parallel processing capabilities, crucial for handling complex neural network AI computations. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Intel’s Aurora achieves exascale to become the fastest AI system appeared first on AI News.

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Marktechpost

AUGUST 25, 2024

Advanced Machine Learning models called Graph Neural Networks (GNNs) process and analyze graph-structured data. The idea of training-free Graph Neural Networks (TFGNNs) has been presented as a solution to these problems. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

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AI News

DECEMBER 15, 2023

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. Explore other upcoming enterprise technology events and webinars powered by TechForge here. And that’s a big struggle,” explains Grande.

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Marktechpost

OCTOBER 16, 2024

A team of researchers from Huazhong University of Science and Technology, hanghai Jiao Tong University, and Renmin University of China introduce IGNN-Solver, a novel framework that accelerates the fixed-point solving process in IGNNs by employing a generalized Anderson Acceleration method, parameterized by a small Graph Neural Network (GNN).

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AI News

JULY 27, 2023

Gcore trained a Convolutional Neural Network (CNN) – a model designed for image analysis – using the CIFAR-10 dataset containing 60,000 labelled images, on these devices. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The event is co-located with Digital Transformation Week.

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Marktechpost

DECEMBER 5, 2024

Researchers from IBM Research, Tel Aviv University, Boston University, MIT, and Dartmouth College have proposed ZipNN, a lossless compression technique specifically designed for neural networks. ZipNN can compress neural network models by up to 33%, with some instances showing reductions exceeding 50% of the original model size.

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AI News

MAY 14, 2024

Pioneering capabilities The introduction of GPT-4o marks a leap from its predecessors by processing all inputs and outputs through a single neural network. Explore other upcoming enterprise technology events and webinars powered by TechForge here. seconds for GPT-3.5 seconds for GPT-4.

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AI News

AUGUST 15, 2023

With its unprecedented efficiency and support for transformer neural networks, we are empowering users across industries to unlock the full potential of AI without compromising on data privacy and security.” Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Marktechpost

AUGUST 5, 2024

This model incorporates a static Convolutional Neural Network (CNN) branch and utilizes a variational attention fusion module to enhance segmentation performance. Hausdorff Distance Using Convolutional Neural Network CNN and ViT Integration appeared first on MarkTechPost. Dice Score and 27.10

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Marktechpost

JULY 23, 2024

Researchers from the University of Tennessee at Chattanooga and the L3S Research Center at Leibniz University Hannover have developed LaMMOn, an end-to-end multi-camera tracking model based on transformers and graph neural networks. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 6, 2024

Current approaches to machine vision, highly dependent on conventional deep neural networks (DNNs) with standard activation functions like ReLU, face limitations in duplicating human-like perception of optical illusions. If you like our work, you will love our newsletter. Don’t Forget to join our 55k+ ML SubReddit.

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Marktechpost

NOVEMBER 16, 2024

Researchers from Google Research, Mountain View, CA, and Google Research, New York, NY have proposed a novel method called Learned Augmented Residual Layer (LAUREL), which revolutionizes the traditional residual connection concept in neural networks. This minimal parameter addition makes LAUREL efficient for large-scale models.

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AI News

AUGUST 9, 2023

These neural networks power the most complex and compute-intensive generative AI applications, spanning from question answering and code generation to audio, video, image synthesis, and speech recognition. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Marktechpost

JANUARY 7, 2025

Advancements in neural networks have brought significant changes across domains like natural language processing, computer vision, and scientific computing. Neural networks often employ higher-order tensor weights to capture complex relationships, but this introduces memory inefficiencies during training.

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Marktechpost

SEPTEMBER 18, 2024

Traditional machine learning methods, such as convolutional neural networks (CNNs), have been employed for this task, but they come with limitations. Moreover, the scale of the data generated through microscopic imaging makes manual analysis impractical in many scenarios. If you like our work, you will love our newsletter.

Convolutional Neural Networks 102

Convolutional Neural Networks Neural Network Automation Machine Learning 102

Marktechpost

SEPTEMBER 22, 2024

Input space mode connectivity in deep neural networks builds upon research on excessive input invariance, blind spots, and connectivity between inputs yielding similar outputs. The phenomenon exists generally, even in untrained networks, as evidenced by empirical and theoretical findings.

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Marktechpost

SEPTEMBER 1, 2024

The implementation of Neural Networks (NNs) is significantly increasing as a means of improving the precision of Molecular Dynamics (MD) simulations. This could lead to new applications in a wide range of scientific fields. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 5, 2024

Recurrent Neural Networks were the trailblazers in natural language processing and set the cornerstone for future advances. RNNs were simple in structure with their contextual memory and constant state size, which promised the capacity to handle long sequence tasks. Don’t Forget to join our 55k+ ML SubReddit.

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Neural Network AI Researcher AI Research Natural Language Processing 82

AssemblyAI

MARCH 15, 2023

Results and Performance In order to evaluate the accuracy and robustness of Conformer-1, we sourced 60+ hours of human labeled audio data covering popular speech domains such as call centers, podcasts, broadcasts, and webinars. "Contextnet: Improving convolutional neural networks for automatic speech recognition with global context."

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Marktechpost

SEPTEMBER 24, 2024

Previously, researchers doubted that neural networks could solve complex visual tasks without hand-designed systems. Training the network took five to six days, leveraging optimized GPU implementations of convolution operations to achieve state-of-the-art performance in object recognition tasks.

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Convolutional Neural Networks Neural Network Computer Vision Deep Learning 60

Marktechpost

AUGUST 31, 2024

Deep neural network training can be sped up by Fully Quantised Training (FQT), which transforms activations, weights, and gradients into lower precision formats. They have experimented with their approach by optimizing popular neural network models, like VGGNet-16 and ResNet-18, using various datasets.

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Marktechpost

JULY 21, 2024

introduce agent symbolic learning framework as an innovative approach for training language agents that draws inspiration from neural network learning. This framework draws an analogy between language agents and neural nets, mapping agent pipelines to computational graphs, nodes to layers, and prompts and tools to weights.

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Artificial Intelligence Artificial Intelligence Neural Network Large Language Models 111

ODSC - Open Data Science

NOVEMBER 28, 2024

Calculating Receptive Field for Convolutional Neural Networks Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. Receptive fields are the backbone of CNN efficacy.

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Convolutional Neural Networks Neural Network LLM Natural Language Processing 52

Marktechpost

JULY 21, 2024

Neural networks and stochastic differential equations (SDEs) are sometimes used to approximate solutions, but these methods can be inefficient and lack the accuracy needed for more complex scenarios. Currently, methods for solving optimal transport problems with complex cost functions are limited.

Neural Network 111

Neural Network Machine Learning Data Science AI 111

Marktechpost

AUGUST 13, 2024

Traditional 2D neural network-based segmentation methods still need to be fully optimized for these high-dimensional imaging modalities, highlighting the need for more advanced approaches to handle the increased data complexity effectively. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

Deep Learning 122

Deep Learning Machine Learning Neural Network Large Language Models 122

Marktechpost

JANUARY 2, 2025

Inspired by the brain, neural networks are essential for recognizing images and processing language. These networks rely on activation functions, which enable them to learn complex patterns. Currently, activation functions in neural networks face significant issues. Dont Forget to join our 60k+ ML SubReddit.

Deep Learning 88

Deep Learning Neural Network LLM Machine Learning 88

Marktechpost

AUGUST 3, 2024

However, these neural networks face challenges in interpretation and scalability. The difficulty in understanding learned representations limits their transparency, while expanding the network scale often proves complex. Also, MLPs rely on fixed activation functions, potentially constraining their adaptability.

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IBM Journey to AI blog

APRIL 16, 2024

You are also invited to join our monthly webinars, where you can gain deeper insights into our products and learn best practices for implementation and usage. Through comprehensive initial and ongoing training sessions, you empower your staff with the skills needed for continuous skill development and confident usage.

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Natural Language Processing Generative AI AI AI 254

Marktechpost

SEPTEMBER 3, 2024

Replicating this process with neural models is particularly difficult due to issues such as maintaining visual fidelity, ensuring stability over extended sequences, and achieving the necessary real-time performance. 2020) have been developed to simulate game environments using neural networks.

Neural Network 60

Neural Network AI AI ML 60

Marktechpost

AUGUST 5, 2024

Conventional methods involve training neural networks from scratch using gradient descent in a continuous numerical space. This shift raises a compelling question: Can a pretrained LLM function as a system parameterized by its natural language prompt, analogous to how neural networks are parameterized by numerical weights?

Machine Learning 119

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Marktechpost

SEPTEMBER 23, 2024

Building massive neural network models that replicate the activity of the brain has long been a cornerstone of computational neuroscience’s efforts to understand the complexities of brain function. These models, which are frequently intricate, are essential for comprehending how neural networks give rise to cognitive functions.

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Marktechpost

OCTOBER 17, 2024

This feature is especially useful for repeated neural network modules like those commonly used in transformers. The regional compilation for torch.compile is another key enhancement that offers a more modular approach to compiling neural networks. If you like our work, you will love our newsletter.

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