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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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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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. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms.

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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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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

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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Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? The two main types of traditional ML algorithms are supervised and unsupervised. Data Preprocessing and Feature Engineering: Traditional ML requires extensive preprocessing to transform datasets as per model requirements.

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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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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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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). Check out the Paper and Reference Article.

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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

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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Marktechpost

DECEMBER 3, 2023

There is a steadily growing list of intriguing properties of neural network (NN) optimization that are not readily explained by classical tools from optimization. Figure 1: Outliers with conflicting signals have a significant impact on the training dynamics of neural networks.

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Marktechpost

DECEMBER 13, 2024

Neural networks have become foundational tools in computer vision, NLP, and many other fields, offering capabilities to model and predict complex patterns. This understanding is essential for designing more efficient training algorithms and enhancing the interpretability and robustness of neural networks.

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Marktechpost

AUGUST 18, 2024

This limitation is significant in applications where human collaboration and explainability are essential. Researchers from the University of Southampton and Kings College London introduced Temporal Reward Decomposition (TRD) to enhance explainability in reinforcement learning. If you like our work, you will love our newsletter.

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Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

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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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Towards AI

JULY 22, 2023

This is similar to how machine learning (ML) can seem at first. 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. But don’t worry!

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Marktechpost

AUGUST 7, 2024

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). The European IVDR recognizes software, including AI and ML algorithms, as part of IVDs. This includes considering patient population, disease conditions, and scanning quality.

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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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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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Unite.AI

SEPTEMBER 4, 2024

Deep Neural Network (DNN) Models: Our core infrastructure utilizes multi-stage DNN models to predict the value of each impression or user. ML-driven Creative Targeting™: For each cohort, we use machine learning in collaboration with our creative team to devise optimal creative strategies.

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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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Unite.AI

DECEMBER 1, 2023

Exploring the Techniques of LIME and SHAP Interpretability in machine learning (ML) and deep learning (DL) models helps us see into opaque inner workings of these advanced models. SHAP ( Source ) Both LIME and SHAP have emerged as essential tools in the realm of AI and ML, addressing the critical need for transparency and trustworthiness.

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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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Marktechpost

SEPTEMBER 22, 2024

Input space mode connectivity in deep neural networks builds upon research on excessive input invariance, blind spots, and connectivity between inputs yielding similar outputs. The phenomenon exists generally, even in untrained networks, as evidenced by empirical and theoretical findings.

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IBM Journey to AI blog

JANUARY 10, 2024

Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming.

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Heartbeat

JUNE 27, 2023

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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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. Machine learning(ML) is evolving at a very fast pace. Machine learning(ML) is evolving at a very fast pace.

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Marktechpost

MARCH 28, 2024

Python: Advanced Guide to Artificial Intelligence This book helps individuals familiarize themselves with the most popular machine learning (ML) algorithms and delves into the details of deep learning, covering topics like CNN, RNN, etc.

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Marktechpost

MAY 5, 2024

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Additionally, Python has emerged as the primary language for various ML tasks. Students learn to implement and analyze models like linear models, kernel machines, neural networks, and graphical models.

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Marktechpost

MARCH 26, 2024

DomainLab is a modular Python package with adjustable regularisation loss terms made especially for neural network training. It is unique because of its decoupled architecture, which keeps the regularisation loss construction and neural network development separate. Also, don’t forget to follow us on Twitter.

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Heartbeat

JUNE 14, 2023

Introduction Deep neural network classifiers have been shown to be mis-calibrated [1], i.e., their prediction probabilities are not reliable confidence estimates. For example, if a neural network classifies an image as a “dog” with probability p , p cannot be interpreted as the confidence of the network’s predicted class for the image.

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Marktechpost

JUNE 4, 2024

Modern Deep Neural Networks (DNNs) are inherently opaque; we do not know how or why these computers arrive at the predictions they do. An emerging area of study called Explainable AI (XAI) has arisen to shed light on how DNNs make decisions in a way that humans can comprehend. Also, don’t forget to follow us on Twitter.

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Marktechpost

NOVEMBER 23, 2024

Concept-driven methods explain the decisions of a model by aligning its representation with human understandable concepts. Researchers from the University of Wisconsin-Madison propose a framework named “Missingness-aware Causal Concept Explainer “to capture the impact of unobserved concepts in data.

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Marktechpost

JUNE 16, 2024

This capability is critical for understanding complex systems like social networks and protein interactions. Topological Neural Networks (TNNs), a subset of TDL, excel in handling higher-order relational data and have shown superior performance in various machine-learning tasks. Check out the Paper and GitHub.

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