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Calculating Receptive Field for Convolutional Neural Networks

ODSC - Open Data Science

Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. CNNs use a three-dimensional convolution layer and a selective type of neuron to compute critical artificial intelligence processes.

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AI and Art: How Artists are Using Artificial Intelligence to Create New Forms of Art?

Becoming Human

AI and Art Artificial Intelligence (AI) has transformed the way we live, work, and communicate, and it is now playing a significant role in the art world. AI and Art: How Artists are Using Artificial Intelligence to Create New Forms of Art?

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The Rise of ChatGPT: A New Era of Artificial Intelligence

Becoming Human

Artificial intelligence (AI) has come a long way in recent years, and one of the most exciting developments in this field is the rise of language models like ChatGPT. Deep learning is a subset of machine learning that involves training artificial neural networks with multiple layers of nodes.

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This AI Paper from Google Unveils How Bayesian Neural Fields Revolutionize Spatiotemporal Forecasting for Large Datasets

Marktechpost

The Bayesian Neural Field (BAYESNF) was introduced, combining the scalability of deep neural networks with the uncertainty quantification abilities of hierarchical Bayesian inference. BAYESNF is based on a Bayesian Neural Network architecture that maps spatiotemporal coordinates to real-valued fields.

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Meet the Research Scientist: Shirley Ho

NYU Center for Data Science

Shirley Ho’s research lies at the intersection of astrophysics, cosmology, and artificial intelligence. Ho’s innovative approach has led to several groundbreaking achievements: Her team at Carnegie Mellon University was the first to apply 3D convolutional neural networks in astrophysics.

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Unraveling Transformer Optimization: A Hessian-Based Explanation for Adam’s Superiority over SGD

Marktechpost

While the Adam optimizer has become the standard for training Transformers, stochastic gradient descent with momentum (SGD), which is highly effective for convolutional neural networks (CNNs), performs worse on Transformer models. This Magazine/Report will be released in late October/early November 2024. Check out the Paper.

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Google AI Researchers Investigate Temporal Distribution Shifts in Deep Learning Models for CTG Analysis

Marktechpost

In response, Google utilizes a deep neural network, CTG-net, to process the time-series data of fetal heart rate (FHR) and uterine contractions (UC) in order to predict fetal hypoxia. The CTG-net model utilizes a convolutional neural network (CNN) architecture to analyze FHR and UC signals, learning their temporal relationships.