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.

Neural Network 104

Neural Network Machine Learning ML AI 104

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).

Neural Network 102

Neural Network Inference Engine ML Artificial Intelligence 102

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

Convolutional Neural Networks 117

Convolutional Neural Networks Neural Network Deep Learning ChatGPT 117

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.

Neural Network 84

Neural Network Machine Learning Metadata Large Language Models 84

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. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

Neural Network 101

Neural Network Computer Vision ML AI 101

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. Don’t Forget to join our 55k+ ML SubReddit. If you like our work, you will love our newsletter.

Neural Network 80

Neural Network Artificial Intelligence Artificial Intelligence Machine Learning 80

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.

Neural Network 92

Neural Network Deep Learning AI AI 92

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.

Neural Network 87

Neural Network Natural Language Processing Computer Vision LLM 87

Marktechpost

JULY 23, 2024

The recent machine-learning (ML) models have remarkably succeeded in short-term weather forecasts. NeuralGCM integrates a differentiable dynamical core with a learned physics module, which uses a neural network to predict the effects of unresolved atmospheric processes. If you like our work, you will love our newsletter.

Machine Learning 113

Machine Learning ML Neural Network AI 113

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 103

Convolutional Neural Networks Neural Network Automation Machine Learning 103

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.

Neural Network 70

Neural Network Computer Scientist Machine Learning AI 70

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.

Neural Network 62

Neural Network Explainability Algorithm ML 62

Marktechpost

NOVEMBER 5, 2024

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

Neural Network 83

Neural Network AI Researcher AI Research Natural Language Processing 83

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.

Convolutional Neural Networks 62

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 62

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.

Neural Network 117

Neural Network AI Researcher AI Research Algorithm 117

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.

Artificial Intelligence 113

Artificial Intelligence Artificial Intelligence Neural Network Large Language Models 113

Marktechpost

OCTOBER 17, 2024

release, the team aims to address several challenges faced by the ML community, focusing primarily on improving computational efficiency, reducing start up times, and enhancing performance scalability for newer hardware. This feature is especially useful for repeated neural network modules like those commonly used in transformers.

Machine Learning 122

Machine Learning Neural Network Data Scientist Inference Engine 122

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 125

Deep Learning Machine Learning Neural Network Large Language Models 125

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 113

Neural Network Machine Learning Data Science AI 113

Marktechpost

JULY 28, 2024

Designing computational workflows for AI applications, such as chatbots and coding assistants, is complex due to the need to manage numerous heterogeneous parameters, such as prompts and ML hyper-parameters. Post-deployment errors require manual updates, adding to the challenge. If you like our work, you will love our newsletter.

Python 142

Python Robotics Neural Network LLM 142

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 90

Deep Learning Neural Network LLM Machine Learning 90

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.

Computer Vision 122

Computer Vision Machine Learning NLP Neural Network 122

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 122

Machine Learning Neural Network LLM Prompt Engineer 122

Marktechpost

SEPTEMBER 21, 2024

Machine Learning in Membrane Science: ML significantly transforms natural sciences, particularly cheminformatics and materials science, including membrane technology. This review focuses on current ML applications in membrane science, offering insights from both ML and membrane perspectives.

Machine Learning 107

Machine Learning Neural Network ML Algorithm 107

Marktechpost

DECEMBER 5, 2024

Although ML models have been very promising in giving faster and more accurate predictions, they fail to represent forecast uncertainty, especially in extreme events. This makes ML-based models less useful in actual real-world applications. The training with ERA5 data from 1979 to 2018 was two-stage scaling from 1 to 0.25 resolution.

Machine Learning 104

Machine Learning Neural Network ML Generative AI 104

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.

Machine Learning 113

Machine Learning Neural Network Computer Scientist Automation 113

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 62

Neural Network AI AI ML 62

Marktechpost

JULY 29, 2024

’s multi-layer feedforward neural networks, deep learning models integrate SNPs, demographics, and clinical data to predict antidepressant responses with high accuracy. Other tools like DeepCare and Doctor AI, using recurrent neural networks, further support prognosis prediction by handling irregularly timed events in EHRs.

Machine Learning 109

Machine Learning Neural Network Deep Learning AI 109

Marktechpost

OCTOBER 23, 2024

The ultimate aim of mechanistic interpretability is to decode neural networks by mapping their internal features and circuits. The paper studies neural network activations and SAE, aiming to minimize reconstruction error while using a few active latents. Don’t Forget to join our 55k+ ML SubReddit.

Neural Network 109

Neural Network Inference Engine Explainability NLP 109

Marktechpost

SEPTEMBER 16, 2024

This discrepancy has reignited debates about whether neural networks possess the essential components of human cognition. This approach aims to simulate large-scale human-like similarity judgment datasets for aligning neural network models with human perception. If you like our work, you will love our newsletter.

Neural Network 109

Neural Network Computer Vision Deep Learning Natural Language Processing 109

Marktechpost

JANUARY 5, 2025

Integration with Graph Neural Networks (GNNs): The final step of the proposed method is to integrate the created knowledge graph with regular Graph Neural Networks. Dont Forget to join our 60k+ ML SubReddit. Also,dont forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

Neural Network 96

Neural Network Large Language Models Automation LLM 96

Marktechpost

JULY 28, 2024

In the past decade, the data-driven method utilizing deep neural networks has driven artificial intelligence success in various challenging applications across different fields. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

Data Analysis 135

Data Analysis AI Modeling Data Science Artificial Intelligence 135

Marktechpost

OCTOBER 16, 2024

One of the core areas of development within machine learning is neural networks, which are especially critical for tasks such as image recognition, language processing, and autonomous decision-making. Model collapse presents a critical challenge affecting neural networks’ scalability and reliability.

Large Language Models 108

Large Language Models Neural Network Machine Learning Inference Engine 108

Marktechpost

AUGUST 28, 2024

Image Source This efficiency is achieved through a highly optimized architecture that blends the strengths of different neural network designs. Specifically, Zamba2-mini employs a hybrid architecture incorporating transformer and Recurrent Neural Network (RNN) elements. If you like our work, you will love our newsletter.

Neural Network 117

Neural Network AI AI ML 117

Marktechpost

JULY 28, 2024

The Sparse Autoencoder (SAE) is a type of neural network designed to efficiently learn sparse representations of data. The Sparse Autoencoder (SAE) neural network efficiently learns sparse data representations. Sparsity helps reduce dimensionality, simplifying complex datasets while keeping crucial information.

Neural Network 117

Neural Network Automation ML AI 117

Marktechpost

SEPTEMBER 15, 2024

It employs a hierarchical approach combining a language model for generating high-level chemical formulae, a diffusion model for deriving detailed crystal structures, and a graph neural network for property prediction. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

Neural Network 102

Neural Network Large Language Models LLM Robotics 102

Marktechpost

JULY 30, 2024

In this approach, data scientists painstakingly transform raw data into formats suitable for ML models. RelBench leverages a novel approach by converting relational databases into graph representations, enabling the use of Graph Neural Networks (GNNs) for predictive tasks. If you like our work, you will love our newsletter.

Deep Learning 120

Deep Learning Neural Network Categorization Data Extraction 120