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

APRIL 6, 2024

In deep learning, a unifying framework to design neural network architectures has been a challenge and a focal point of recent research. The researchers tackle the core issue of the absence of a general-purpose framework capable of addressing both the specification of constraints and their implementations within neural network models.

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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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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

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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Neural Network Machine Learning ML AI 104

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.

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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

JANUARY 2, 2024

Exploring pre-trained models for research often poses a challenge in Machine Learning (ML) and Deep Learning (DL). One solution to simplify the visualization of ML/DL models is the open-source tool called Netron. Without this framework, comprehending the model’s structure becomes cumbersome for AI researchers.

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Marktechpost

MAY 10, 2024

Redundant execution introduces the concept of a hybrid (convolutional) neural network designed to facilitate reliable neural network execution for safe and dependable AI. The method has scope for further extension to more complex neural network architectures and applications with additional optimization.

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Marktechpost

JULY 7, 2024

Artificial neural networks (ANNs) traditionally lack the adaptability and plasticity seen in biological neural networks. In conclusion, LNDPs represent a framework for evolving self-organizing neural networks that incorporate lifelong plasticity and structural adaptability.

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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

APRIL 5, 2024

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Transformer architectures, exemplified by models like ChatGPT, have revolutionized natural language processing tasks.

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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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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.

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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

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.

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Marktechpost

JANUARY 13, 2024

The challenge of interpreting the workings of complex neural networks, particularly as they grow in size and sophistication, has been a persistent hurdle in artificial intelligence. The traditional methods of explaining neural networks often involve extensive human oversight, limiting scalability.

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Marktechpost

APRIL 4, 2024

A deep Neural network is crucial in synthesizing photorealistic images and videos using large-scale image and video generative models. Also, the neural network weight, convolution, or linear layers remain the same for different conditions. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Neural Network AI AI ML 125

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.

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Neural Network Deep Learning Data Mining Natural Language Processing 113

Marktechpost

DECEMBER 21, 2023

ReLoRA accomplishes a high-rank update, delivering a performance akin to conventional neural network training. link] Scaling laws have been identified, demonstrating a strong power-law dependence between network size and performance across different modalities, supporting overparameterization and resource-intensive neural networks.

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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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Marktechpost

MAY 2, 2024

This study’s research area is artificial intelligence (AI) and machine learning, specifically focusing on neural networks that can understand binary code. This dataset allowed them to train neural networks to understand binary code more effectively. If you like our work, you will love our newsletter.

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Neural Network Machine Learning Large Language Models Automation 122

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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Neural Network Machine Learning Algorithm AI Research 143

Marktechpost

NOVEMBER 7, 2023

Neural networks have become indispensable tools in various fields, demonstrating exceptional capabilities in image recognition, natural language processing, and predictive analytics. The sum of these vectors is then passed to the next layer, creating a sparse and discrete bottleneck within the network.

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Neural Network Natural Language Processing AI AI 117

Marktechpost

JULY 9, 2024

Given this, Spiking Neural Networks (SNNs) are a potential paradigm for energy-efficient time series processing thanks to their great accuracy and efficiency. Don’t Forget to join our 46k+ ML SubReddit The post Efficient Continual Learning for Spiking Neural Networks with Time-Domain Compression appeared first on MarkTechPost.

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Neural Network Continuous Learning Algorithm ML 107

Marktechpost

MAY 16, 2024

Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on the same data domain converge to similar learned patterns. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Neural Network Machine Learning ML Artificial Intelligence 117

Marktechpost

DECEMBER 17, 2023

Neural networks, the marvels of modern computation, encounter a significant hurdle when confronted with tabular data featuring heterogeneous columns. The essence of this challenge lies in the networks’ inability to handle diverse data structures within these tables effectively.

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Neural Network Deep Learning Data Analysis AI Research 109

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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Convolutional Neural Networks Neural Network Deep Learning ML 138

Marktechpost

MAY 11, 2024

Graph Neural Networks (GNNs) are crucial in processing data from domains such as e-commerce and social networks because they manage complex structures. Traditionally, GNNs operate on data that fits within a system’s main memory. Check out the Paper. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Neural Network ML AI AI 104

Marktechpost

DECEMBER 26, 2023

Conventional methods in this field have heavily relied on deep neural networks (DNNs) due to their exceptional ability to model complex patterns. Reduction of model complexity through neural network sparsification. Gradual decrease of nonlinearity in neural models, optimizing them for efficient use in automatic control.

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Neural Network Robotics Automation AI Research 109

Marktechpost

NOVEMBER 25, 2023

Addressing this, Jason Eshraghian from UC Santa Cruz developed snnTorch, an open-source Python library implementing spiking neural networks, drawing inspiration from the brain’s remarkable efficiency in processing data. Traditional neural networks lack the elegance of the brain’s processing mechanisms.

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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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Neural Network Python Artificial Intelligence Artificial Intelligence 138

Marktechpost

SEPTEMBER 8, 2023

It offers a versatile platform not only for neural networks but also for handling a wide range of tasks, including ODEs, SDEs, linear solves, and more. The post Meet Equinox: A JAX library for Neural Networks and sciML appeared first on MarkTechPost. All Credit For This Research Goes To the Researchers on This Project.

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Neural Network Machine Learning Data Science AI Research 104

Marktechpost

JULY 10, 2024

Recurrent Neural Networks (RNNs), on the other hand, have linear complexity, which increases their computational efficiency. Don’t Forget to join our 46k+ ML SubReddit The post Revolutionizing Recurrent Neural Networks RNNs: How Test-Time Training TTT Layers Outperform Transformers appeared first on MarkTechPost.

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Neural Network Large Language Models Machine Learning ML 103

Marktechpost

MAY 17, 2024

Natural language processing (NLP) has advanced significantly thanks to neural networks, with transformer models setting the standard. Researchers from the Toyota Research Institute have introduced Scalable UPtraining for Recurrent Attention (SUPRA), a method to convert pre-trained transformers into recurrent neural networks (RNNs).

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Neural Network NLP Natural Language Processing AI 109

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.

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Neural Network Deep Learning Computer Vision Big Data 135

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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