Marktechpost

APRIL 18, 2024

When utilizing the popular backpropagation as the default learning method, training deep neural networks—which can include hundreds of layers—can be a laborious process that can last weeks. This becomes an even bigger issue when dealing with massive neural networks, where a lot of data needs to be sent.

Neural Network 105

Neural Network Algorithm Machine Learning Deep Learning 105

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.

Neural Network 143

Neural Network Machine Learning Algorithm AI Researcher 143

Marktechpost

DECEMBER 15, 2023

Complex tasks like text or picture synthesis, segmentation, and classification are being successfully handled with the help of neural networks. However, it can take days or weeks to obtain adequate results from neural network training due to its computing demands. If you like our work, you will love our newsletter.

Neural Network 130

Neural Network Machine Learning Algorithm Deep Learning 130

Marktechpost

JANUARY 24, 2024

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. However, the dawn of deep learning brought about a paradigm shift in data representation, introducing complex neural networks that generate more sophisticated data representations known as embeddings.

Neural Network 135

Neural Network Deep Learning Computer Vision Big Data 135

Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

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. This is achieved through a parallel prefix scan algorithm that allows Aaren to process multiple context tokens simultaneously while updating its state efficiently.

Neural Network 133

Neural Network Machine Learning Algorithm Robotics 133

Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications. Check out the Research Article and Project Page.

Neural Network 127

Neural Network Large Language Models AI AI 127

Marktechpost

MARCH 23, 2024

Don’t Forget to join our 39k+ ML SubReddit The post FeatUp: A Machine Learning Algorithm that Upgrades the Resolution of Deep Neural Networks for Improved Performance in Computer Vision Tasks appeared first on MarkTechPost. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

Computer Vision 132

Computer Vision Neural Network Machine Learning Algorithm 132

Marktechpost

MAY 25, 2024

Understanding neural networks is vital for trust, ethical concerns like algorithmic bias, and scientific applications requiring model validation. Based on the Kolmogorov-Arnold Networks (KANs) offer improved interpretability and accuracy based on the Kolmogorov-Arnold theorem.

Neural Network 119

Neural Network Machine Learning Algorithm ML 119

Marktechpost

JANUARY 24, 2024

However, deep neural networks are inaccurate and can produce unreliable outcomes. It can improve deep neural networks’ reliability in inverse imaging issues. The model works by executing forward–backward cycles using a physical forward model and has an iterative-trained neural network.

Neural Network 115

Neural Network Deep Learning Data Mining Natural Language Processing 115

Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

Neural Network 126

Neural Network Machine Learning Natural Language Processing Computer Vision 126

Marktechpost

OCTOBER 1, 2023

Researchers have recently developed Temporal Graph Neural Networks (TGNNs) to take advantage of temporal information in dynamic graphs, building on the success of Graph Neural Networks (GNNs) in learning static graph representation. If you like our work, you will love our newsletter.

Neural Network 131

Neural Network Algorithm AI Researcher AI Research 131

Marktechpost

MARCH 20, 2024

These sophisticated algorithms, designed to mimic human language, are at the heart of modern technological conveniences, powering everything from digital assistants to content creation tools. The development and refinement of large language models (LLMs) mark a significant step in the progress of machine learning.

Neural Network 126

Neural Network Large Language Models Conversational AI Machine Learning 126

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.

Machine Learning 230

Machine Learning Neural Network Algorithm Computer Vision 230

Marktechpost

FEBRUARY 8, 2024

With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

Neural Network 134

Neural Network Deep Learning Machine Learning AI 134

Marktechpost

OCTOBER 8, 2023

Yes, the field of study is called Neural networks. Researchers at the University of Copenhagen present a graph neural network type of encoding in which the growth of a policy network is controlled by another network running in each neuron. They call it a Neural Developmental Program (NDP).

Neural Network 129

Neural Network Deep Learning Automation Algorithm 129

Marktechpost

OCTOBER 13, 2023

In the realm of deep learning, the challenge of developing efficient deep neural network (DNN) models that combine high performance with minimal latency across a variety of devices remains. However, this approach tends to overlook optimizing the search space itself. Check out the Paper and Reference Article. We are also on WhatsApp.

Neural Network 131

Neural Network Deep Learning Algorithm Automation 131

Marktechpost

JULY 7, 2024

Deep neural networks (DNNs) come in various sizes and structures. The specific architecture selected along with the dataset and learning algorithm used, is known to influence the neural patterns learned. It shows that these networks naturally learn structured representations, especially when they start with small weights.

Neural Network 116

Neural Network Explainability Deep Learning Machine Learning 116

Marktechpost

NOVEMBER 27, 2024

At its core, machine learning algorithms seek to identify patterns within data, enabling computers to learn and adapt to new information. In the same way, ML uses data to find patterns and helps computers learn how to make predictions or decisions based on those patterns. This ability to learn makes ML incredibly powerful.

Machine Learning 116

Machine Learning Algorithm Neural Network Convolutional Neural Networks 116

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

Neural Network 162

Neural Network Machine Learning NLP Natural Language Processing 162

Marktechpost

JANUARY 19, 2024

Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes must be better understood. To conclude, the researchers have introduced a precise and interpretable neural network architecture based on DNA methylation for age estimation. Check out the Paper.

Neural Network 116

Neural Network Deep Learning Explainable AI Explainability 116

Marktechpost

MARCH 4, 2024

The proposed method, ViSNet, introduces a vector-scalar interactive graph neural network framework designed to enhance molecular geometry modeling. Evaluation across various datasets demonstrates ViSNet’s superior performance compared to existing algorithms in predicting molecular properties and simulating molecular dynamics.

Neural Network 110

Neural Network Algorithm ML AI 110

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

ML 188

ML Deep Learning Neural Network Machine Learning 188

Marktechpost

JULY 9, 2024

Furthermore, many applications now need AI algorithms to adapt to individual users while ensuring privacy and reducing internet connectivity. Given this, Spiking Neural Networks (SNNs) are a potential paradigm for energy-efficient time series processing thanks to their great accuracy and efficiency.

Neural Network 109

Neural Network Continuous Learning Algorithm ML 109

Marktechpost

NOVEMBER 25, 2024

Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. Researchers have developed alternative learning mechanisms tailored for spiking neural networks (SNNs) and neuromorphic hardware to address these challenges.

Neural Network 85

Neural Network Deep Learning Algorithm Large Language Models 85

Marktechpost

JANUARY 25, 2024

It relies on the distortion of objects in a density-varying medium due to light refraction, with digital image correlation or optical flow algorithms used for analysis. Existing algorithms, mostly based on cross-correlation, are optimized for PIV and provide sparse velocity vectors. Michigan Ave.,

Neural Network 104

Neural Network Algorithm Machine Learning ML 104

Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

Convolutional Neural Networks 289

Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 289

Marktechpost

FEBRUARY 12, 2024

Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. As a result, many people are interested in finding ways to maximize the energy efficiency of DNNs through algorithm and hardware optimization. There are three notable characteristics of PDC in general.

Convolutional Neural Networks 133

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 133

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).

Neural Network 105

Neural Network Machine Learning Convolutional Neural Networks Categorization 105

Marktechpost

JANUARY 9, 2024

It mentions the under-utilization of the Siamese neural network technique in recent studies on multimodal medical image classification, which motivates this study. TwinCNN combines a twin convolutional neural network framework with a hybrid binary optimizer for multimodal breast cancer digital image classification.

Convolutional Neural Networks 119

Convolutional Neural Networks Neural Network Deep Learning Algorithm 119

Marktechpost

DECEMBER 11, 2023

Moreover, since the models depend on the knowledge of climate scientists to improve equations, parameterizations, and algorithms, NWP forecast accuracy remains the same with additional data. Using historical data, like the ERA5 reanalysis dataset, deep neural networks are trained to forecast future weather conditions.

Neural Network 126

Neural Network Deep Learning Explainability Algorithm 126

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.

Neural Network 110

Neural Network Natural Language Processing Machine Learning Data Quality 110

Marktechpost

JANUARY 8, 2024

The framework utilizes a dynamic programming-based graph partition algorithm and a Simulated-Annealing-based (SA-based) approach for optimization. Gemini’s mapping component uses the SA algorithm with five operators tailored to efficiently explore the LP spatial mapping space. Also, don’t forget to follow us on Twitter.

Neural Network 115

Neural Network Algorithm AI AI 115

Unite.AI

AUGUST 29, 2023

In the past few years, Artificial Intelligence (AI) and Machine Learning (ML) have witnessed a meteoric rise in popularity and applications, not only in the industry but also in academia. It’s the major reason why its difficult to build a standard ML architecture for IoT networks.

Neural Network 246

Neural Network ML Auto-classification Algorithm 246

Towards AI

JANUARY 9, 2024

Now, I know, I know, we all want to work straight away on the latest and greatest technologies, which all work with Neural Networks. You see, AI is just the most general term for decision-making algorithms and does not necessarily mean ML. In other words, we all want to get directly into Deep Learning.

ML 119

ML Neural Network Deep Learning Machine Learning 119

IBM Journey to AI blog

JANUARY 10, 2024

AI operates on three fundamental components: data, algorithms and computing power. Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. What is artificial intelligence and how does it work?

Artificial Intelligence 229

Artificial Intelligence Artificial Intelligence Neural Network Algorithm 229

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.

Neural Network 79

Neural Network Machine Learning Computer Vision AI 79