Towards AI

APRIL 8, 2024

Interconnected graphical data is all around us, ranging from molecular structures to social networks and design structures of cities. Graph Neural Networks (GNNs) are emerging as a powerful method of modeling and learning the spatial and graphical structure of such data. An illustration of GNN: Figure 1.

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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

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

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

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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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

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

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

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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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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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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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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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

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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Mlearning.ai

FEBRUARY 4, 2024

This is the second part of my Neural Networks 101 series, in this blog we are going to discuss about the training of machine learning models. Now that everything is in order, let’s move on to our topic: To minimize a predetermined loss function, neural network models’ parameters are iteratively adjusted during training.

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Marktechpost

MARCH 4, 2024

The proposed method, ViSNet, introduces a vector-scalar interactive graph neural network framework designed to enhance molecular geometry modeling. Join our 38k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. Also, don’t forget to follow us on Twitter and Google News.

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Marktechpost

MARCH 31, 2024

To address this, various feature extraction methods have emerged: point-based networks and sparse convolutional neural networks CNNs Convolutional Neural Networks. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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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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Towards AI

AUGUST 21, 2023

Deep Learning vs. Neural Networks: What’s the Difference? Amidst this backdrop, we often hear buzzwords like artificial intelligence (AI), machine learning (ML), deep learning, and neural networks thrown around almost interchangeably. Machine Learning (ML): Next, machine learning takes the spotlight.

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Marktechpost

APRIL 24, 2024

Don’t Forget to join our 40k+ ML SubReddit The post Researchers at MIT Propose ‘MAIA’: An Artificial Intelligence System that Uses Neural Network Models to Automate Neural Model Understanding Tasks appeared first on MarkTechPost. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Marktechpost

MARCH 26, 2024

Graph neural networks (GNNs) have revolutionized how researchers analyze and learn from data structured in complex networks. These models capture the intricate relationships inherent in graphs, which are omnipresent in social networks, molecular structures, and communication networks, to name a few areas.

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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).

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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.

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Marktechpost

JANUARY 19, 2024

Neural network-based methods in estimating biological age have shown high accuracy but lack interpretability, prompting the development of a biologically informed tool for interpretable predictions in prostate cancer and treatment resistance. The most noteworthy result was probably obtained for the pan-tissue dataset.

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Marktechpost

DECEMBER 18, 2023

In a groundbreaking study, MIT researchers have delved into the realm of deep neural networks, aiming to unravel the mysteries of the human auditory system. The foundation of this research builds upon prior work where neural networks were trained to perform specific auditory tasks, such as recognizing words from audio signals.

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Marktechpost

DECEMBER 30, 2023

While PDE discovery seeks to determine a PDE’s coefficients from data, PDE solvers use neural networks to approximate a known PDE’s solution. The team has shared that operator learning makes use of a variety of neural network topologies, and it’s important to comprehend which ones are chosen.

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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.

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Marktechpost

OCTOBER 22, 2023

As highlighted in recent studies, including one conducted by a team of scientists, artificial neural networks designed to mimic the various functions of the human visual and auditory systems often exhibit invariances that do not align with those found in human sensory perception. If you like our work, you will love our newsletter.

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Marktechpost

SEPTEMBER 15, 2023

The traditional theory of how neural networks learn and generalize is put to the test by the occurrence of grokking in neural networks. This behavior is basically grokking in neural networks. Generalizing Solution: With this approach, the neural network is well-suited to generalizing to new data.

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Marktechpost

SEPTEMBER 1, 2023

These intricate neural networks, with their complex processes and hidden layers, have captivated researchers and practitioners while obscuring their inner workings. The crux of the challenge stems from the inherent complexity of deep neural networks. A 20-layer feedforward neural network is trained on Fashion-MNIST.

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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. Check out the Paper.

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Mlearning.ai

MAY 30, 2023

Recurrent Neural Networks (RNNs) have become a potent tool for analysing sequential data in the large subject of artificial intelligence and machine learning. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

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Marktechpost

JANUARY 25, 2024

Ypsilanti, USA, have developed a method employing Physics-Informed Neural Networks (PINNs) to deduce complete 3D velocity and pressure fields from 3D temperature snapshots obtained through Tomo-BOS imaging. In PINN, the trainable variables are the parameters of the neural network, not the conventional control variables.

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Marktechpost

FEBRUARY 21, 2024

Graph-based machine learning is undergoing a significant transformation, largely propelled by the introduction of Graph Neural Networks (GNNs). These networks have been pivotal in harnessing the complexity of graph-structured data, offering innovative solutions across various domains.

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Marktechpost

JULY 2, 2023

Optical neural networks have been suggested as solutions that provide better efficiency and latency than neural-network implementations on digital computers, among other ways. What happens if you run a Transformer model with an optical neural network?* appeared first on MarkTechPost.

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Marktechpost

OCTOBER 28, 2023

Machine learning models like neural networks have been instrumental in advancing scientific discovery and experimental design in biological sciences. The post NYU Researchers have Created a Neural Network for Genomics that can Explain How it Reaches its Predictions appeared first on MarkTechPost.

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Marktechpost

MARCH 11, 2024

Deep Neural Networks (DNNs) excel in enhancing surgical precision through semantic segmentation and accurately identifying robotic instruments and tissues. Join our 38k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. Also, don’t forget to follow us on Twitter and Google News.

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