ML @ CMU

JANUARY 2, 2025

The brain may have evolved inductive biases that align with the underlying structure of natural tasks, which explains its high efficiency and generalization abilities in such tasks. We use a model-free actor-critic approach to learning, with the actor and critic implemented using distinct neural networks.

Neural Network 134

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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.

Neural Network 119

Neural Network Large Language Models Explainability Generative AI 119

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.

Neural Network 87

Neural Network Explainable AI Explainability Machine Learning 87

Towards AI

JANUARY 20, 2025

Integrating Bayesian Theory, State-Space Dynamics, and Neural Network Structures for Enhanced Probabilistic Forecasting This member-only story is on us. Thats where the Bayesian State-Space Neural Network (BSSNN) offers a novel solution. Upgrade to access all of Medium.

Neural Network 99

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Machine Learning Mastery

NOVEMBER 22, 2024

This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task. Unity makes strength.

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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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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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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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AI News

JANUARY 28, 2025

Operating virtually rather than from a single physical base, Cognitive Labs will explore AI technologies such as Graph Neural Networks (GNNs), Active Learning, and Large-Scale Language Models (LLMs). Ericsson has launched Cognitive Labs, a research-driven initiative dedicated to advancing AI for telecoms.

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Unite.AI

DECEMBER 17, 2024

By combining the power of neural networks with the logic of symbolic AI, it could solve some of the reliability problems generative AI faces. To make matters worse, when AI makes mistakes, it doesnt explain itself. It combines two strengths: neural networks that recognize patterns and symbolic AI that uses logic to reason.

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Unite.AI

FEBRUARY 25, 2025

As AI systems increasingly power mission-critical applications across industries such as finance, defense, healthcare, and autonomous systems, the demand for trustworthy, explainable, and mathematically rigorous reasoning has never been higher. For industries reliant on neural networks, ensuring robustness and safety is critical.

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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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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

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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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.

Neural Network 126

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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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Unite.AI

FEBRUARY 17, 2025

Last year, they introduced AlphaGeometry , an AI system that combines the predictive power of neural networks with the structured logic of symbolic reasoning to tackle complex geometry problems. The neural language model helps the system predict new geometric constructs, while symbolic AI applies formal logic to generate proofs.

Neural Network 127

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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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Neural Network LLM Explainability NLP 105

Towards AI

DECEMBER 18, 2024

They chase the hype Neural Networks, Transformers, Deep Learning, and, who can forget AI and fall flat. You're not ready for neural networks if you cant explain Linear Regression or Decision Trees. More about me here. But heres the truth: Most beginners get lost in the noise. The secret? Lets get started.

Machine Learning 105

Machine Learning Neural Network Deep Learning Explainability 105

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.

Neural Network 104

Neural Network LLM Explainability NLP 104

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.

Neural Network 103

Neural Network LLM Explainability NLP 103

Marktechpost

MARCH 8, 2025

Current Challenge with Traditional CAM Conventional CAM methods typically illustrate general regions influencing a neural networks predictions but frequently fail to distinguish fine details necessary for differentiating closely related classes. Experimental Validation B.1.

Explainability 54

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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

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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Unite.AI

NOVEMBER 22, 2024

It helps explain how AI models, especially LLMs, process information and make decisions. By using a specific type of neural network called sparse autoencoders (SAEs) , Gemma Scope breaks down these complex processes into simpler, more understandable parts. To tackle this challenge, DeepMind has created a tool called Gemma Scope.

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Large Language Models Neural Network AI AI 113

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.

Neural Network 109

Neural Network Explainability ML AI 109

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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Explainable AI Explainability Neural Network Data Scientist 117

Unite.AI

FEBRUARY 10, 2025

Can you explain how NeuroSplit dynamically optimizes compute resources while maintaining user privacy and performance? NeuroSplit is fundamentally device-agnostic, cloud-agnostic, and neural network-agnostic. But AI shouldn't be limited by which end-user device someone happens to use. Think about what this means for developers.

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NVIDIA

JANUARY 6, 2025

NVIDIA GPUs and platforms are at the heart of this transformation, Huang explained, enabling breakthroughs across industries, including gaming, robotics and autonomous vehicles (AVs). The latest generation of DLSS can generate three additional frames for every frame we calculate, Huang explained.

Robotics 144

Robotics Explainability AI AI 144

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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