Marktechpost

FEBRUARY 4, 2024

Meta-learning, a burgeoning field in AI research, has made significant strides in training neural networks to adapt swiftly to new tasks with minimal data. This technique centers on exposing neural networks to diverse tasks, thereby cultivating versatile representations crucial for general problem-solving.

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Marktechpost

JANUARY 23, 2024

There are two major challenges in visual representation learning: the computational inefficiency of Vision Transformers (ViTs) and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

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Marktechpost

SEPTEMBER 3, 2024

Recent neural architectures remain inspired by biological nervous systems but lack the complex connectivity found in the brain, such as local density and global sparsity. Researchers from Microsoft Research Asia introduced CircuitNet, a neural network inspired by neuronal circuit architectures.

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Analytics Vidhya

FEBRUARY 24, 2023

Or maybe you’re curious about how to implement a neural network using PyTorch. Introduction Are you interested in learning about Apache Spark and how it has transformed big data processing? Or perhaps you want to explore the exciting world of AI and its career opportunities?

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Marktechpost

MARCH 9, 2024

The evolution of artificial intelligence, particularly in the realm of neural networks, has significantly advanced our data processing and analysis capabilities. Among these advancements, the efficiency of training and deploying deep neural networks has become a paramount focus.

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Marktechpost

SEPTEMBER 6, 2024

Spiking Neural Networks (SNNs), a family of artificial neural networks that mimic the spiking behavior of biological neurons, have been in discussion in recent times. These networks provide a fresh method for working with temporal data, identifying the complex relationships and patterns seen in sequences.

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Marktechpost

OCTOBER 18, 2024

Deep neural networks are powerful tools that excel in learning complex patterns, but understanding how they efficiently compress input data into meaningful representations remains a challenging research problem. Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

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Marktechpost

AUGUST 4, 2024

Representational similarity measures are essential tools in machine learning, used to compare internal representations of neural networks. These measures help researchers understand learning dynamics, model behaviors, and performance by providing insights into how different neural network layers and architectures process information.

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Marktechpost

MAY 28, 2024

Traditionally, Recurrent Neural Networks (RNNs) have been used for their ability to process sequential data efficiently despite their limitations in parallel processing. Rapid machine learning advancement has highlighted existing models’ limitations, particularly in resource-constrained environments.

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Flipboard

NOVEMBER 11, 2024

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.

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Marktechpost

JUNE 30, 2024

Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation. Join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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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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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

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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Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? The two main types of traditional ML algorithms are supervised and unsupervised. Data Preprocessing and Feature Engineering: Traditional ML requires extensive preprocessing to transform datasets as per model requirements.

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Marktechpost

JUNE 9, 2024

xECGArch uniquely separates short-term (morphological) and long-term (rhythmic) ECG features using two independent Convolutional Neural Networks CNNs. Researchers at the Institute of Biomedical Engineering, TU Dresden, developed a deep learning architecture, xECGArch, for interpretable ECG analysis.

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Marktechpost

SEPTEMBER 10, 2024

The intersection of computational physics and machine learning has brought significant progress in understanding complex systems, particularly through neural networks. Traditional neural networks, including many adapted to consider Hamiltonian properties, often need help with these systems’ high dimensionality and complexity.

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Unite.AI

MARCH 15, 2024

Nevertheless, addressing the cost-effectiveness of ML models for business is something companies have to do now. For businesses beyond the realms of big tech, developing cost-efficient ML models is more than just a business process — it's a vital survival strategy. Challenging Nvidia, with its nearly $1.5

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Marktechpost

OCTOBER 4, 2024

Evaluated Models Ready Tensor’s benchmarking study categorized the 25 evaluated models into three main types: Machine Learning (ML) models, Neural Network models, and a special category called the Distance Profile model. Prominent models include Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).

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Marktechpost

NOVEMBER 25, 2024

Researchers have developed alternative learning mechanisms tailored for spiking neural networks (SNNs) and neuromorphic hardware to address these challenges. A spiking neural network implements these computations using a leaky integrate-and-fire neuron model on Intel’s Loihi chip.

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Marktechpost

OCTOBER 7, 2024

Recurrent neural networks (RNNs) have been foundational in machine learning for addressing various sequence-based problems, including time series forecasting and natural language processing. The post Revisiting Recurrent Neural Networks RNNs: Minimal LSTMs and GRUs for Efficient Parallel Training appeared first on MarkTechPost.

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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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Unite.AI

OCTOBER 27, 2024

The ecosystem has rapidly evolved to support everything from large language models (LLMs) to neural networks, making it easier than ever for developers to integrate AI capabilities into their applications. Key Features: Hardware-accelerated ML operations using WebGL and Node.js environments. TensorFlow.js TensorFlow.js

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Marktechpost

APRIL 17, 2024

Don’t Forget to join our 40k+ ML SubReddit Want to get in front of 1.5 Work with us here The post Google AI Proposes TransformerFAM: A Novel Transformer Architecture that Leverages a Feedback Loop to Enable the Neural Network to Attend to Its Latent Representations appeared first on MarkTechPost. Million AI Audience?

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Marktechpost

DECEMBER 13, 2024

Neural networks have become foundational tools in computer vision, NLP, and many other fields, offering capabilities to model and predict complex patterns. This understanding is essential for designing more efficient training algorithms and enhancing the interpretability and robustness of neural networks.

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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 Dice Score and 27.10

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Unite.AI

SEPTEMBER 21, 2023

Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models. AI and machine learning (ML) algorithms are capable of the following: Analyzing transaction patterns to detect fraudulent activities made by bots. What is Blockchain?

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Unite.AI

AUGUST 28, 2024

However, while many cyber vendors claim to bring AI to the fight, machine learning (ML) – a less sophisticated form of AI – remains a core part of their products. ML is unfit for the task. The distinction between ML and DL-based solutions becomes evident when examining their ability to identify and prevent known and unknown threats.

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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. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms. And that’s a big struggle,” explains Grande.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Marktechpost

DECEMBER 1, 2024

Graph Neural Networks (GNNs) are a rapidly advancing field in machine learning, specifically designed to analyze graph-structured data representing entities and their relationships. These networks have been widely used in social network analysis, recommendation systems, and molecular data interpretation applications.

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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. Don’t Forget to join our 55k+ ML SubReddit.

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Marktechpost

NOVEMBER 12, 2024

This article discusses a recent study that aims to improve competitor retrieval with the help of graph neural networks. JPMorgan Chase researchers propose JPMorgan Proximity Embedding for Competitor Detection, a Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs.

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Unite.AI

SEPTEMBER 4, 2024

Deep Neural Network (DNN) Models: Our core infrastructure utilizes multi-stage DNN models to predict the value of each impression or user. ML-driven Creative Targeting™: For each cohort, we use machine learning in collaboration with our creative team to devise optimal creative strategies.

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Marktechpost

OCTOBER 31, 2024

Don’t Forget to join our 55k+ ML SubReddit. Trending ] LLMWare Introduces Model Depot: An Extensive Collection of Small Language Models (SLMs) for Intel PCs The post XElemNet: A Machine Learning Framework that Applies a Suite of Explainable AI (XAI) for Deep Neural Networks in Materials Science appeared first on MarkTechPost.

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Marktechpost

OCTOBER 7, 2024

Modern machine learning (ML) phenomena such as double descent and benign overfitting have challenged long-standing statistical intuitions, confusing many classically trained statisticians. Various researchers have attempted to unravel the complexities of modern ML phenomena.

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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.

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