Marktechpost

SEPTEMBER 1, 2023

Artificial intelligence’s allure has long been shrouded in mystique, especially within the enigmatic realm of deep learning. These intricate neural networks, with their complex processes and hidden layers, have captivated researchers and practitioners while obscuring their inner workings.

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Marktechpost

MARCH 23, 2024

AI researchers are taking the game to a new level with geometric deep learning. DeepMind Researchers introduce TacticAI, an AI assistant designed to optimize one of football’s biggest set-piece weapons: the corner kick. All credit for this research goes to the researchers of this project.

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Marktechpost

JULY 2, 2023

The exponentially expanding scale of deep learning models is a major force in advancing the state-of-the-art and a source of growing worry over the energy consumption, speed, and, therefore, feasibility of massive-scale deep learning. What happens if you run a Transformer model with an optical neural network?*

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Marktechpost

DECEMBER 30, 2023

The remarkable potentials of Artificial Intelligence (AI) and Deep Learning have paved the way for a variety of fields ranging from computer vision and language modeling to healthcare, biology, and whatnot. SciML consists of three primary subfields, which include PDE solvers, PDE discovery, and operator learning.

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Marktechpost

OCTOBER 13, 2023

In the realm of deep learning, the challenge of developing efficient deep neural network (DNN) models that combine high performance with minimal latency across a variety of devices remains. Join our AI Channel on Whatsapp. If you like our work, you will love our newsletter. We are also on WhatsApp.

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Marktechpost

SEPTEMBER 10, 2023

Researchers think that high-speed testing using Deep Learning models can help us understand these effects better and speed up catalyst development. The way a catalyst’s surface is shaped matters for certain chemical reactions due to various properties of the catalyst, which we study in Surface Chemistry.

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Marktechpost

DECEMBER 15, 2023

Several new innovations have been made possible because of the advancements in the field of Artificial intelligence and Deep Learning. Complex tasks like text or picture synthesis, segmentation, and classification are being successfully handled with the help of neural networks.

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Marktechpost

NOVEMBER 8, 2023

Overconfidence is a prevalent issue, particularly in the context of deep neural networks. The team has shared the two primary features of Fortuna that greatly improve deep learning uncertainty quantification. Deep neural networks that are being trained from the start can be trained using these techniques.

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Marktechpost

OCTOBER 8, 2023

Can we adapt these hierarchy organization and parallel processing techniques in deep learning? Yes, the field of study is called Neural networks. This provides an advantage to NDP as it can operate upon any neural network of arbitrary size or architecture. Join our AI Channel on Whatsapp.

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Marktechpost

JULY 12, 2023

Artificial neural networks have advanced significantly over the past few decades, propelled by the notion that more network complexity results in better performance. Modern technology has amazing processing capacity, enabling neural networks to perform these jobs excellently and efficiently.

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Marktechpost

SEPTEMBER 8, 2023

Researchers suggest a new approach to design using heuristic optimization and artificial neural networks to simplify the optimization process drastically. A deep neural network model replaced the 3D electromagnetic simulation of a Si-based MZM. If you like our work, you will love our newsletter.

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Marktechpost

DECEMBER 28, 2023

Purdue University’s researchers have developed a novel approach, Graph-Based Topological Data Analysis (GTDA), to simplify interpreting complex predictive models like deep neural networks. The method was also applied to study chest X-ray diagnostics and compare deep-learning frameworks, showcasing its versatility.

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Marktechpost

JANUARY 4, 2024

Qu Kun from the University of Science and Technology of the Chinese Academy of Sciences has created a solution called Spatial Architecture Characterization by Deep Learning (SPACEL). The post Meet SPACEL: A New Deep-Learning-based Analysis Toolkit for Spatial Transcriptomics appeared first on MarkTechPost.

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Marktechpost

DECEMBER 25, 2023

A considerable amount of research has been done to address this problem, focusing mainly on using artificial neural networks. Therefore, the authors of this research paper from SLAC National Accelerator Laboratory, USA have introduced PtychoPINN. If you like our work, you will love our newsletter.

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Marktechpost

SEPTEMBER 8, 2023

It offers a versatile platform not only for neural networks but also for handling a wide range of tasks, including ODEs, SDEs, linear solves, and more. The post Meet Equinox: A JAX library for Neural Networks and sciML appeared first on MarkTechPost. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 16, 2023

Researchers from MIT investigated the scaling behavior of large chemical language models, focusing on both generative pre-trained transformers (GPT) for chemistry (ChemGPT) and graph neural network force fields (GNNs). All credit for this research goes to the researchers of this project. Check out the Paper.

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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. Join our AI Channel on Whatsapp.

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Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

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Marktechpost

NOVEMBER 23, 2023

Researchers have introduced a novel approach using topological data analysis (TDA), to solve the issue. These models, including machine learning, neural networks, and AI models, have become standard tools in various scientific fields but are often difficult to interpret due to their extensive parameterization.

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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. Join our AI Channel on Whatsapp. If you like our work, you will love our newsletter.

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Marktechpost

JANUARY 5, 2024

Existing methods based on deep learning tend to model boundaries as discrete, rasterized maps, needing more resilience and adaptability for varied image resolutions and aspect ratios. Recent advances in boundary detection have predominantly employed deep learning techniques focusing on discrete representations.

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Marktechpost

AUGUST 21, 2023

The deep learning method, a machine learning technique inspired by the human brain, has applications in various domains, such as image processing, image recognition, speech recognition, and language translation. The authors have introduced P-DNN as an innovative solution to the reconfigurability problems of ONNs.

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Marktechpost

DECEMBER 30, 2023

A new research paper from CSIRO, Australia, addresses these challenges by introducing a novel solution, ML-SEISMIC, a physics-informed deep neural network designed to align stress orientation data with an elastic model autonomously. In geological investigations, conventional inversion processes have long been the norm.

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Marktechpost

NOVEMBER 16, 2023

A new research paper presents a deep learning-based classifier for age-related macular degeneration (AMD) stages using retinal optical coherence tomography (OCT) scans. The research details creating a deep learning-based system for automated AMD detection and staging using retinal OCT scans.

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Marktechpost

JULY 2, 2023

Implicit neural representations (INRs) or neural fields are coordinate-based neural networks representing a field, such as a 3D scene, by mapping 3D coordinates to color and density values in 3D space. They perform well in many research areas, including generation, inference, and classification. Want to support us?

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Marktechpost

DECEMBER 19, 2023

Advancements in deep learning have influenced a wide variety of scientific and industrial applications in artificial intelligence. Recurrent Neural Networks (RNNs) and Transformers are the most common methods; each has advantages and disadvantages. All credit for this research goes to the researchers of this project.

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Marktechpost

DECEMBER 29, 2023

Consequently, the researchers of Plant Phenomics have introduced BarbNet, a deep-learning model designed specifically for the automated detection and phenotyping of barbs in microscopic images of awns. Researchers used binary cross-entropy loss and Dice Coefficient (DC) for training and validating the model.

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Marktechpost

OCTOBER 4, 2023

In a groundbreaking development, a team of researchers from the Bioengineering Center and the Computational Health Center at Helmholtz Munich, in collaboration with the Technical University of Munich, has unveiled a deep-learning framework called DeepMB. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 26, 2023

The capacity of a model to use inputs at inference time to modify its behavior without updating its weights to tackle problems that were not present during training is known as in-context learning or ICL. Since then, a substantial amount of research has examined or documented instances of ICL.

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Marktechpost

DECEMBER 15, 2023

This method involves the application of a generative neural network, specifically a Generative Adversarial Network (GAN), a form of AI. According to researchers, this is the reason for developing GAN, an AI-based generative neural network trained using high-resolution radar precipitation fields.

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AI Weekly

OCTOBER 26, 2023

cryptopolitan.com Applied use cases Alluxio rolls out new filesystem built for deep learning Alluxio Enterprise AI is aimed at data-intensive deep learning applications such as generative AI, computer vision, natural language processing, large language models and high-performance data analytics.

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Marktechpost

OCTOBER 10, 2023

Deep learning, a machine learning subset, automatically learns complex representations from the input. Convolutional neural networks (CNNs) and vision transformers (ViT), two examples of deep learning models for computer vision, analyze signals by assuming planar (flat) regions.

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Marktechpost

NOVEMBER 24, 2023

Researchers from Genentech introduced tumor dynamic neural-ODE (TDNODE) as a pharmacology-informed neural network for enhancing tumor dynamic modeling in oncology drug development. It employs a recurrent neural network to determine initial conditions and an attention-based LSTM to assess tumor kinetic parameters.

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Mlearning.ai

JULY 17, 2023

How taking inspiration from the brain can help us create Neural Networks. Data Scientists spend a lot of time and resources in improving the architecture of their AI Models. As such, there is a lot of research into figuring out different architectures and how different design decisions impact the performance of models.

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AI Weekly

NOVEMBER 23, 2023

Connect with industry leaders, heads of state, entrepreneurs and researchers to explore the next wave of transformative AI technologies. igamingbusiness.com Ethics What’s the smart way of moving forward with AI? Be ready for a twofer. singularitynet.io

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Marktechpost

SEPTEMBER 1, 2023

Researchers have been exploring behavioral and physiological biometrics for enhancing mobile device security. Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. It builds upon previous research in this field and identifies trends in authentication dynamics.

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

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