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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Analytics Vidhya

MAY 5, 2019

Introduction “How did your neural network produce this result?” It’s easy to explain how. The post A Guide to Understanding Convolutional Neural Networks (CNNs) using Visualization appeared first on Analytics Vidhya. ” This question has sent many data scientists into a tizzy.

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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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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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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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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

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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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

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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Marktechpost

SEPTEMBER 30, 2023

This redundancy consumes extra energy and leads to an expanded chip footprint, raising concerns about space efficiency and scalability in large-scale optical neural networks (ONNs) and optimization problem solvers. Efforts to address this issue have been made, with solutions such as a pseudo-real-value MZI mesh.

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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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Towards AI

MARCH 17, 2024

Alongside this, there is a second boom in XAI or Explainable AI. Explainable AI is focused on helping us poor, computationally inefficient humans understand how AI “thinks.” We will then explore some techniques for building glass-box or explainable models. Ultimately these definitions end up being almost circular!

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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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NYU Center for Data Science

JANUARY 31, 2024

The idea that neural networks undergo distinct developmental phases during training has long been a subject of debate and fascination. Published in the Transactions on Machine Learning Research (TMLR) , Hu’s work offers new insights into the training dynamics of neural networks.

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ODSC - Open Data Science

JANUARY 22, 2024

These agents autonomously experiment on and explain the behavior of neural networks, marking a departure from traditional human-led approaches. This work opens new avenues for understanding and advancing the capabilities of neural networks.

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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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Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

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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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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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Marktechpost

DECEMBER 5, 2023

The Hierarchically Gated Recurrent Neural Network (HGRN) technique developed by researchers from the Shanghai Artificial Intelligence Laboratory and MIT CSAI addresses the challenge of enhancing sequence modeling by incorporating forget gates in linear RNNs. Check out the Paper , Github, and Project.

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Unite.AI

APRIL 30, 2024

It includes deciphering neural network layers , feature extraction methods, and decision-making pathways. These systems rely heavily on neural networks to process vast amounts of information. During training, neural networks learn patterns from extensive datasets.

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Marktechpost

JANUARY 28, 2024

Deep learning models, such as multilayer perceptrons, recurrent neural networks, and transformers, have been employed to forecast the fitness of genotypes based on experimental data. The study revealed that deep learning models are impressively effective, with some explaining over 90% of fitness variance in the data.

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Marktechpost

JULY 2, 2023

Thus, there is a growing demand for explainability methods to interpret decisions made by modern machine learning models, particularly neural networks. CRAFT addresses this limitation by harnessing modern machine learning techniques to unravel the complex and multi-dimensional visual representations learned by neural networks.

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Viso.ai

FEBRUARY 8, 2024

Restricted Boltzmann Machines A Restricted Boltzmann Machine is a two-layer probabilistic neural network. Benefits of DBNs Deep belief networks use probabilistic modeling and a supervised learning approach to offer certain benefits over conventional neural networks. Faster training and better results.

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Heartbeat

MARCH 1, 2023

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. A dataset of labeled images is used to train the network, with each image given a particular class or label.

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Marktechpost

MAY 5, 2024

The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. Students learn to implement and analyze models like linear models, kernel machines, neural networks, and graphical models.

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Machine Learning Python Neural Network Convolutional Neural Networks 118

Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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Marktechpost

JULY 6, 2023

This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play, and the need to understand their differences become more apparent. On the other hand, explainable AI models are very complicated deep learning models that are too complex for humans to understand without the aid of additional methods.

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Towards AI

JANUARY 27, 2023

Deep Learning Explained: Perceptron The key concept behind every neural network. A bit of history Beginning Back in 1943, McCulloch and Pitts published a paper entitled A logical calculus of the ideas immanent in nervous activity — known today as the first mathematical model of a neural network.

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Marktechpost

OCTOBER 8, 2023

By utilizing finely developed neural network architectures, we have models that are distinguished by extraordinary accuracy within their respective sectors. Despite their accurate performance, we must still fully understand how these neural networks function. We have new advancements that have been there with each day.

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Mlearning.ai

APRIL 24, 2023

One of the most popular and efficient approaches for object detection is the You Only Look Once version 5 (YOLOv5) neural network architecture. However, the YOLOv5 neural network architecture has several hyperparameters that can significantly affect its performance. BECOME a WRITER at MLearning.ai

Explainability 52

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Marktechpost

MAY 1, 2024

While human intelligence relies on biological neural networks and operates at slower speeds, AI systems leverage digital processors for rapid data processing and seamless communication. Human introspection is limited, making it challenging to explain unconscious thoughts.

Neural Network 112

Neural Network Artificial Intelligence Artificial Intelligence Explainability 112

Mlearning.ai

MAY 11, 2023

Generative Pre-trained Transformer (GPT) Photo by Levart_Photographer on Unsplash Developed by OpenAI, GPT, which stands for “Generative Pre-trained Transformer,” is a neural network that has the ability to generate human-like language, making it an impressive tool for natural language processing (NLP).

Neural Network 52

Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

Towards AI

MAY 23, 2023

Policy Gradient Method As explained above, Policy Gradient (PG) methods are algorithms that aim to learn the optimal policy function directly in a Markov Decision Processes setting (S, A, P, R, γ). a neural network), so we can control its outputs by changing its parameters.

Explainability 98

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ML Review

MARCH 31, 2019

Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3%

Neural Network 52

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Towards AI

JULY 22, 2023

Our job is to explain complicated tech terms in a simple day-to-day language, so everyone can understand. Neural Networks: The Brainy Network Picture your brain. It’s a massive network of neurons connected by synapses. This intricate network is the basis for all our thoughts, decisions, and actions.

Explainability 98

Explainability ML Neural Network Machine Learning 98

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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