Analytics Vidhya

JANUARY 6, 2024

In a groundbreaking development, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have introduced a novel method leveraging artificial intelligence (AI) agents to automate the explanation of intricate neural networks.

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Unite.AI

MAY 8, 2024

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems. Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Unite.AI

JULY 21, 2023

Later he explained that sampling is a state of mind, which is true. The post Artists Behind Neural Network Models: The Impact of AI on the Creator Economy appeared first on Unite.AI. However, the artist was quick to pay $4 million for the track. Rap music makes the clearance of rights a fertile ground in the music business.

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Analytics Vidhya

SEPTEMBER 27, 2022

Introduction My last blog discussed the “Training of a convolutional neural network from scratch using the custom dataset.” ” In that blog, I have explained: how to create a dataset directory, train, test and validation dataset splitting, and training from scratch. This blog is […].

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AI News

FEBRUARY 16, 2024

“While a traditional Transformer functions as one large neural network, MoE models are divided into smaller ‘expert’ neural networks,” explained Demis Hassabis, CEO of Google DeepMind. This specialisation massively enhances the model’s efficiency.”

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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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Becoming Human

MARCH 16, 2023

Photo by Paulius Andriekus on Unsplash Welcome back to the next part of this Blog Series on Graph Neural Networks! The following section will provide a little introduction to PyTorch Geometric , and then we’ll use this library to construct our very own Graph Neural Network!

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Marktechpost

OCTOBER 28, 2023

However, a common limitation of many machine learning models in this field is their lack of interpretability – they can predict outcomes accurately but struggle to explain how they arrived at those predictions. This innovative model has the potential to significantly enhance our understanding of this fundamental process.

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Unite.AI

JUNE 15, 2024

Graduate student Diego Aldarondo collaborated with DeepMind researchers to train an artificial neural network (ANN) , which serves as the virtual brain, using the powerful machine learning technique deep reinforcement learning.

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

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. DINN extends DWLR by adding feature interaction terms, creating a neural network architecture. The author provides code and data for reproducibility.

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

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Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. DINN extends DWLR by adding feature interaction terms, creating a neural network architecture. The author provides code and data for reproducibility.

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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.

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Towards AI

NOVEMBER 11, 2024

This issue is especially common in large language models (LLMs), the neural networks that drive these AI tools. Interestingly, there’s a historical parallel that helps explain this limitation. Bender, a linguistics professor, explains: if you see the word “cat,” you might recall memories or associations related to real cats.

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Flipboard

NOVEMBER 11, 2024

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.

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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

OCTOBER 31, 2024

However, explainability is an issue as they are ‘black boxes,’ so to say, hiding their inner working. XElemNet, the proposed solution, employs explainable AI techniques, particularly layer-wise relevance propagation (LRP), and integrates them into ElemNet. Check out the Paper.

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Marktechpost

APRIL 24, 2024

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. ELI5 is a Python package that helps debug machine learning classifiers and explain their predictions.

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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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Unite.AI

JUNE 25, 2023

However, assimilating the understanding of physics into the realm of neural networks has proved challenging. Concurrently, physics-based research sought to unravel the physical principles underlying many computer vision challenges.

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Unite.AI

AUGUST 28, 2024

Deep learning (DL), the most advanced form of AI, is the only technology capable of preventing and explaining known and unknown zero-day threats. Unlike ML, DL is built on neural networks, enabling it to self-learn and train on raw data. Can you explain the inspiration behind DIANNA and its key functionalities?

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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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AI News

AUGUST 13, 2024

. “While the novel neural-symbolic AI approaches developed by the SingularityNET AI team decrease the need for data, processing and energy somewhat relative to standard deep neural nets, we still need significant supercomputing facilities,” SingularityNET CEO Ben Goertzel explained to LiveScience in a recent written statement.

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Marktechpost

JUNE 9, 2024

Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features. xECGArch uniquely separates short-term (morphological) and long-term (rhythmic) ECG features using two independent Convolutional Neural Networks CNNs.

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Unite.AI

OCTOBER 24, 2024

Modern AI models, particularly those based on deep learning and neural networks , are incredibly power-hungry. Unlike traditional neural networks, which rely on complex mathematical computations and massive datasets, Tsetlin Machines employ a more straightforward, rule-based approach.

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Unite.AI

MARCH 11, 2024

Interpretable and Explainable: Using multiple components allows us to interpret how each component contributes to the final output, making these systems interpretable and transparent. The goal is to merge the intuitive data processing abilities of neural networks with the structured, logical reasoning of symbolic AI.

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PyImageSearch

OCTOBER 28, 2024

Home Table of Contents NeRFs Explained: Goodbye Photogrammetry? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps Next Steps Citation Information NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?

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AI News

DECEMBER 18, 2023

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular. They then analyse and assess risks to ensure compliance with regulations. “There’s a lot of misconceptions, definitely.

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AI News

DECEMBER 15, 2023

And that’s a big struggle,” explains Grande. Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy.

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Marktechpost

DECEMBER 11, 2023

These models use systems of differential equations that explain thermodynamics and fluid flow and may be integrated across time to produce projections for the future. Using historical data, like the ERA5 reanalysis dataset, deep neural networks are trained to forecast future weather conditions.

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Marktechpost

SEPTEMBER 26, 2023

Microsoft researchers propose a groundbreaking solution to these challenges in their recent “Neural Graphical Models” paper presented at the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023).

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Unite.AI

OCTOBER 5, 2023

This is your third AI book, the first two being: “Practical Deep Learning: A Python-Base Introduction,” and “Math for Deep Learning: What You Need to Know to Understand Neural Networks” What was your initial intention when you set out to write this book? AI as neural networks is merely (!)

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Marktechpost

MARCH 23, 2024

To reduce the memory footprint and further speed up the training of FeatUp’s implicit network, the spatially varying features are compressed to their top k=128 principal components. This optimization accelerates training time by a remarkable 60× for models like ResNet-50 and facilitates larger batches without compromising feature quality.

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Towards AI

JANUARY 4, 2024

Source from the author At least, that is my case, so I put together this post to explain the different concepts in graph neural networks (GNNs) in a way that is more intuitive and beginner-friendly, complemented with a code example.

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