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This AI Paper from King’s College London Introduces a Theoretical Analysis of Neural Network Architectures Through Topos Theory

Marktechpost

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. Despite their widespread usage, the theoretical foundations of transformers have yet to be fully explored.

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Microsoft Researchers Propose Neural Graphical Models (NGMs): A New Type of Probabilistic Graphical Models (PGM) that Learns to Represent the Probability Function Over the Domain Using a Deep Neural Network

Marktechpost

Many graphical models are designed to work exclusively with continuous or categorical variables, limiting their applicability to data that spans different types. Moreover, specific restrictions, such as continuous variables not being allowed as parents of categorical variables in directed acyclic graphs (DAGs), can hinder their flexibility.

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This AI Tool Explains How AI ‘Sees’ Images And Why It Might Mistake An Astronaut For A Shovel

Marktechpost

It is known that, similar to the human brain, AI systems employ strategies for analyzing and categorizing images. Thus, there is a growing demand for explainability methods to interpret decisions made by modern machine learning models, particularly neural networks.

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Introduction to Graph Neural Networks

Heartbeat

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

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#59: The Agentic AI Era, Smolagents, and a “Gatekeeper” Agent Prototype

Towards AI

Meme shared by rucha8062 TAI Curated section Article of the week Graph Neural Networks: Unlocking the Power of Relationships in Predictions By Shenggang Li This article explores Graph Neural Networks (GNNs), focusing on their ability to analyze connected data. Meme of the week!

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NLP Rise with Transformer Models | A Comprehensive Analysis of T5, BERT, and GPT

Unite.AI

One-hot encoding is a process by which categorical variables are converted into a binary vector representation where only one bit is “hot” (set to 1) while all others are “cold” (set to 0). Functionality : Each encoder layer has self-attention mechanisms and feed-forward neural networks.

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Scientists Develop ‘Material Fingerprinting’ Method Using AI and X-ray Technology

Unite.AI

These fingerprints can then be analyzed by a neural network, unveiling previously inaccessible information about material behavior. As Argonne postdoctoral researcher James (Jay) Horwath explains, “The way we understand how materials move and change over time is by collecting X-ray scattering data.”