Marktechpost

APRIL 18, 2024

When utilizing the popular backpropagation as the default learning method, training deep neural networks—which can include hundreds of layers—can be a laborious process that can last weeks. This becomes an even bigger issue when dealing with massive neural networks, where a lot of data needs to be sent.

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Marktechpost

JANUARY 25, 2024

It relies on the distortion of objects in a density-varying medium due to light refraction, with digital image correlation or optical flow algorithms used for analysis. Existing algorithms, mostly based on cross-correlation, are optimized for PIV and provide sparse velocity vectors. Michigan Ave.,

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Marktechpost

NOVEMBER 6, 2024

Current approaches to machine vision, highly dependent on conventional deep neural networks (DNNs) with standard activation functions like ReLU, face limitations in duplicating human-like perception of optical illusions. QT-DNN uniquely uses a physical quantum random number generator to ensure unbiased visual information processing.

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TensorFlow

FEBRUARY 6, 2024

Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases.

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Marktechpost

FEBRUARY 8, 2024

With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). Despite these efforts, the search for an optimal solution remains ongoing.

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Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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Marktechpost

MAY 6, 2024

Convolutional Neural Networks (CNNs) have become the benchmark for computer vision tasks. Capsule Networks (CapsNets), first introduced by Hinton et al. Capsule Networks (CapsNets), first introduced by Hinton et al. They hold significant potential for revolutionizing the field of computer vision.

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Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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Unite.AI

SEPTEMBER 12, 2024

In a significant leap forward, researchers at the University of Southern California (USC) have developed a new artificial intelligence algorithm that promises to revolutionize how we decode brain activity. DPAD: A New Approach to Neural Decoding The DPAD algorithm represents a paradigm shift in how we approach neural decoding.

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Marktechpost

MARCH 23, 2024

Although deep features have many applications in computer vision, they often need more spatial resolution to directly perform dense prediction tasks like segmentation and depth prediction due to models that aggressively pool information by models over large areas. It provides sub-feature information to train the upsampler.

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Pickl AI

SEPTEMBER 5, 2024

Summary: Convolutional Neural Networks (CNNs) are essential deep learning algorithms for analysing visual data. Introduction Neural networks have revolutionised Artificial Intelligence by mimicking the human brai n’s structure to process complex data. What are Convolutional Neural Networks?

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Google Research AI blog

AUGUST 17, 2023

Posted by Hussein Hazimeh, Research Scientist, Athena Team, and Riade Benbaki, Graduate Student at MIT Modern neural networks have achieved impressive performance across a variety of applications, such as language, mathematical reasoning , and vision. In standard neural networks, each weight defines a connection between two neurons.

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Marktechpost

JANUARY 24, 2024

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. However, the dawn of deep learning brought about a paradigm shift in data representation, introducing complex neural networks that generate more sophisticated data representations known as embeddings.

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Marktechpost

JULY 9, 2024

Furthermore, many applications now need AI algorithms to adapt to individual users while ensuring privacy and reducing internet connectivity. This is the capacity to learn from new situations constantly without losing any of the information that has already been discovered. Join our Telegram Channel and LinkedIn Gr oup.

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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. RNNs are designed to handle sequences of varying lengths by maintaining an internal state that captures information across time steps.

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Pickl AI

SEPTEMBER 15, 2024

Summary: Siamese Neural Networks use twin subnetworks to compare pairs of inputs and measure their similarity. Introduction Neural networks form the backbone of Deep Learning , allowing machines to learn from data by mimicking the human brain’s structure. What is a Siamese Neural Network?

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Marktechpost

OCTOBER 1, 2023

Both spatial and temporal information are crucial in spatial-temporal applications like traffic and weather forecasting. Researchers have created Memory-based Temporal Graph Neural Networks (M-TGNNs) that store node-level memory vectors to summarize independent node history to make up for the lost history.

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Marktechpost

OCTOBER 8, 2023

The brain is hierarchically organized, with lower-level sensory processing areas sending information to higher-level cognitive and decision-making regions. The brain processes information in parallel, with different regions and networks simultaneously working on various aspects of perception, cognition, and motor control.

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Towards AI

AUGUST 21, 2023

Deep Learning vs. Neural Networks: What’s the Difference? The rapid evolution of technology is molding our everyday existence as businesses turn more and more to sophisticated algorithms for efficiency. ML employs algorithms that learn patterns from data to perform tasks without explicit programming.

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Viso.ai

MAY 17, 2024

Neural Networks have changed the way we perform model training. Neural networks, sometimes referred to as Neural Nets, need large datasets for efficient training. So, what if we have a neural network that can adapt itself to new data and has less complexity? What is a Liquid Neural Network?

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Marktechpost

JANUARY 19, 2024

Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes must be better understood. CpG methylation beta values enter the input layer, and information propagates through the network, connecting nodes based on shared annotations in ReactomeDB.

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Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. So, let’s get started! What are Graphs?

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Marktechpost

MARCH 4, 2024

The proposed method, ViSNet, introduces a vector-scalar interactive graph neural network framework designed to enhance molecular geometry modeling. Evaluation across various datasets demonstrates ViSNet’s superior performance compared to existing algorithms in predicting molecular properties and simulating molecular dynamics.

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Bugra Akyildiz

FEBRUARY 3, 2024

And their approach is: Expander Graphs: Exphormer uses "expander graphs" as a sparse but informative representation of the original graph. Intermediate Nodes: To compensate for the information loss from reduced edge density, Exphormer introduces intermediate nodes within the expander graph.

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ODSC - Open Data Science

SEPTEMBER 18, 2024

Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. Even if the conclusions are not wholly accurate, data scientists can learn from how much and what kind of information it pools from the field.

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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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Marktechpost

NOVEMBER 2, 2024

iP-VAE achieves Bayesian posterior inference through its membrane potential dynamics, resembling a spiking version of the Locally Competitive Algorithm for sparse coding. The model’s updates are expressed in log rates, mimicking membrane potentials in spiking neural networks. Don’t Forget to join our 55k+ ML SubReddit.

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Marktechpost

FEBRUARY 12, 2024

Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. As a result, many people are interested in finding ways to maximize the energy efficiency of DNNs through algorithm and hardware optimization. They work well with preexisting DCNNs and are computationally efficient.

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Marktechpost

APRIL 26, 2024

Here, we explore the algorithms that drive neuromorphic computing, its potential use cases, and its diverse applications. Algorithms in Neuromorphic Computing Neuromorphic computing leverages unique algorithms to mimic neurobiological architectures inherent to the nervous system.

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Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

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Flipboard

DECEMBER 27, 2023

Indeed, when officials in Seine-Saint-Denis, one of the districts hosting the Olympics, presented information about a preliminary AI-powered video surveillance system that would detect and issue fines for antisocial behavior such as littering, residents raised their hands and asked why it wasn’t yet on their streets.

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Marktechpost

AUGUST 21, 2023

The results of today’s neural networks in fields as diverse as language, mathematics, and vision are remarkable. These networks, however, typically employ elaborate structures that are resource-intensive to run. Each weight in a typical neural network specifies the link between two neurons.

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Mlearning.ai

JULY 17, 2023

How taking inspiration from the brain can help us create Neural Networks. One of the most promising avenues for this kind of research is to learn from evolution and biological systems to improve the design of AI Models (after all Neural Networks were based on our brains).

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Marktechpost

JULY 15, 2023

Since the emergence of large-scale OT and Wasserstein GANs, machine learning has increasingly embraced using neural networks to solve optimum transport (OT) issues. The theoretical underpinnings of the algorithm make its output more easily understood than competing methods.

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Marktechpost

JANUARY 9, 2024

This limitation restricts the diagnosis process by relying on insufficient information and neglecting a comprehensive understanding of the physical conditions associated with the disease. TwinCNN combines a twin convolutional neural network framework with a hybrid binary optimizer for multimodal breast cancer digital image classification.

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Marktechpost

MAY 15, 2024

Recent advancements have seen the integration of neural networks with combinatorial solvers to learn from contextual features and combinatorial solutions end-to-end. Their approach leverages the Floyd-Warshall algorithm, renowned for its ability to solve all-to-all shortest path problems in a single run based on shortcuts.

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Marktechpost

NOVEMBER 18, 2024

Recommender systems have been widely applied for studying user preferences; however, they face significant challenges in accurately capturing user preferences, particularly in the context of neural graph collaborative filtering. Moreover, malicious attacks that introduce fake interactions further deteriorate the recommendation quality.

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