Marktechpost

OCTOBER 18, 2024

Deep neural networks are powerful tools that excel in learning complex patterns, but understanding how they efficiently compress input data into meaningful representations remains a challenging research problem. Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

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Marktechpost

APRIL 24, 2024

Don’t Forget to join our 40k+ ML SubReddit The post Researchers at MIT Propose ‘MAIA’: An Artificial Intelligence System that Uses Neural Network Models to Automate Neural Model Understanding Tasks appeared first on MarkTechPost. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 16, 2024

Artificial Intelligence (AI) has been making significant strides over the past few years, with the emergence of Large Language Models (LLMs) marking a major milestone in its growth. The author talks about machine intelligence’s historical background and provides beginners with information on how advanced algorithms work.

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Marktechpost

MARCH 28, 2024

Artificial Intelligence (AI) has been making significant strides over the past few years, with the emergence of Large Language Models (LLMs) marking a major milestone in its growth. The author talks about machine intelligence’s historical background and provides beginners with information on how advanced algorithms work.

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Marktechpost

JUNE 22, 2024

Neural networks, despite their theoretical capability to fit training sets with as many samples as they have parameters, often fall short in practice due to limitations in training procedures. Key technical aspects include the use of various neural network architectures (MLPs, CNNs, ViTs) and optimizers (SGD, Adam, AdamW, Shampoo).

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Marktechpost

JUNE 28, 2024

Artificial Intelligence (AI) has been making significant strides over the past few years, with the emergence of Large Language Models (LLMs) marking a major milestone in its growth. The author talks about machine intelligence’s historical background and provides beginners with information on how advanced algorithms work.

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Marktechpost

AUGUST 4, 2024

Representational similarity measures are essential tools in machine learning, used to compare internal representations of neural networks. These measures help researchers understand learning dynamics, model behaviors, and performance by providing insights into how different neural network layers and architectures process information.

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Marktechpost

SEPTEMBER 3, 2024

Recent neural architectures remain inspired by biological nervous systems but lack the complex connectivity found in the brain, such as local density and global sparsity. Researchers from Microsoft Research Asia introduced CircuitNet, a neural network inspired by neuronal circuit architectures.

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Marktechpost

NOVEMBER 19, 2024

Neural networks have traditionally operated as static models with fixed structures and parameters once trained, a limitation that hinders their adaptability to new or unforeseen scenarios. While flexible models and network pruning have been explored to address these challenges, they come with constraints.

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Marktechpost

SEPTEMBER 6, 2024

Spiking Neural Networks (SNNs), a family of artificial neural networks that mimic the spiking behavior of biological neurons, have been in discussion in recent times. These networks provide a fresh method for working with temporal data, identifying the complex relationships and patterns seen in sequences.

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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. Transformer architectures, exemplified by models like ChatGPT, have revolutionized natural language processing tasks.

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Marktechpost

APRIL 6, 2024

In deep learning, a unifying framework to design neural network architectures has been a challenge and a focal point of recent research. The researchers tackle the core issue of the absence of a general-purpose framework capable of addressing both the specification of constraints and their implementations within neural network models.

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Marktechpost

JULY 7, 2024

Artificial neural networks (ANNs) traditionally lack the adaptability and plasticity seen in biological neural networks. Developing ANNs that can self-organize, learn from experiences, and adapt throughout their lifetime is crucial for advancing the field of artificial intelligence (AI).

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Marktechpost

APRIL 26, 2024

More sophisticated methods like TARNet, Dragonnet, and BCAUSS have emerged, leveraging the concept of representation learning with neural networks. In some cases, the neural network might detect and rely on interactions between variables that don’t actually have a causal relationship.

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Marktechpost

MAY 30, 2024

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It includes labs on feature engineering with BigQuery ML, Keras, and TensorFlow.

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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. If you like our work, you will love our newsletter.

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Mlearning.ai

JUNE 10, 2023

Artificial intelligence (AI) has witnessed a groundbreaking advancement in the form of neural networks, revolutionizing the way machines… Continue reading on MLearning.ai »

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Marktechpost

MAY 2, 2024

This study’s research area is artificial intelligence (AI) and machine learning, specifically focusing on neural networks that can understand binary code. This dataset allowed them to train neural networks to understand binary code more effectively. If you like our work, you will love our newsletter.

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Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. One of the central challenges in this field is the extended time needed to train complex neural networks.

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Marktechpost

NOVEMBER 16, 2024

Researchers from Google Research, Mountain View, CA, and Google Research, New York, NY have proposed a novel method called Learned Augmented Residual Layer (LAUREL), which revolutionizes the traditional residual connection concept in neural networks. Don’t Forget to join our 55k+ ML SubReddit.

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Marktechpost

MARCH 9, 2024

The evolution of artificial intelligence, particularly in the realm of neural networks, has significantly advanced our data processing and analysis capabilities. Among these advancements, the efficiency of training and deploying deep neural networks has become a paramount focus.

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Marktechpost

OCTOBER 16, 2024

A team of researchers from Huazhong University of Science and Technology, hanghai Jiao Tong University, and Renmin University of China introduce IGNN-Solver, a novel framework that accelerates the fixed-point solving process in IGNNs by employing a generalized Anderson Acceleration method, parameterized by a small Graph Neural Network (GNN).

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Marktechpost

NOVEMBER 25, 2023

In artificial intelligence, efficiency, and environmental impact have become paramount concerns. Addressing this, Jason Eshraghian from UC Santa Cruz developed snnTorch, an open-source Python library implementing spiking neural networks, drawing inspiration from the brain’s remarkable efficiency in processing data.

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Marktechpost

NOVEMBER 12, 2024

This article discusses a recent study that aims to improve competitor retrieval with the help of graph neural networks. JPMorgan Chase researchers propose JPMorgan Proximity Embedding for Competitor Detection, a Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs.

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Marktechpost

OCTOBER 4, 2024

Evaluated Models Ready Tensor’s benchmarking study categorized the 25 evaluated models into three main types: Machine Learning (ML) models, Neural Network models, and a special category called the Distance Profile model. Prominent models include Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).

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Marktechpost

JULY 24, 2023

With the advent of Machine Learning (ML) and Deep Learning (DL), image and video editing have been progressively studied through several neural network architectures. Image and video editing are two of the most popular applications for computer users.

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Marktechpost

MAY 25, 2024

Understanding neural networks is vital for trust, ethical concerns like algorithmic bias, and scientific applications requiring model validation. Based on the Kolmogorov-Arnold Networks (KANs) offer improved interpretability and accuracy based on the Kolmogorov-Arnold theorem.

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Marktechpost

JANUARY 24, 2024

Artificial Intelligence has witnessed a revolution, largely due to advancements in deep learning. This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. Data was historically represented in simpler forms, often as hand-crafted feature vectors.

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Marktechpost

JANUARY 2, 2024

Exploring pre-trained models for research often poses a challenge in Machine Learning (ML) and Deep Learning (DL). One solution to simplify the visualization of ML/DL models is the open-source tool called Netron. Without this framework, comprehending the model’s structure becomes cumbersome for AI researchers.

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Marktechpost

AUGUST 25, 2024

Advanced Machine Learning models called Graph Neural Networks (GNNs) process and analyze graph-structured data. The idea of training-free Graph Neural Networks (TFGNNs) has been presented as a solution to these problems. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

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Pickl AI

OCTOBER 20, 2024

Summary: Learning Artificial Intelligence involves mastering Python programming, understanding Machine Learning principles, and engaging in practical projects. Introduction Artificial Intelligence (AI) is transforming industries worldwide, with applications in healthcare, finance, and technology. dollars by 2030.

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Marktechpost

MAY 10, 2024

Redundant execution introduces the concept of a hybrid (convolutional) neural network designed to facilitate reliable neural network execution for safe and dependable AI. The method has scope for further extension to more complex neural network architectures and applications with additional optimization.

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Marktechpost

AUGUST 2, 2024

AI, particularly through ML and DL, has advanced medical applications by automating complex tasks. ML algorithms learn from data to improve over time, while DL uses neural networks to handle large, complex datasets. Further research is required to address these challenges and advance AI’s role in healthcare.

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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. Check out the Research Article and Project Page.

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Marktechpost

OCTOBER 7, 2024

Recurrent neural networks (RNNs) have been foundational in machine learning for addressing various sequence-based problems, including time series forecasting and natural language processing. The post Revisiting Recurrent Neural Networks RNNs: Minimal LSTMs and GRUs for Efficient Parallel Training appeared first on MarkTechPost.

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Marktechpost

OCTOBER 22, 2023

In the pursuit of replicating the complex workings of the human sensory systems, researchers in neuroscience and artificial intelligence face a persistent challenge: the disparity in invariances between computational models and human perception. If you like our work, you will love our newsletter. We are also on WhatsApp.

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Marktechpost

JULY 21, 2024

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling the creation of language agents capable of autonomously solving complex tasks. introduce agent symbolic learning framework as an innovative approach for training language agents that draws inspiration from neural network learning.

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