Marktechpost

FEBRUARY 28, 2024

Parameter generation, distinct from visual generation, aims to create neural network parameters for task performance. Researchers from the National University of Singapore, University of California, Berkeley, and Meta AI Research have proposed neural network diffusion , a novel approach to parameter generation.

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Marktechpost

JANUARY 9, 2024

Deep learning methods have been widely employed for early disease detection to tackle this challenge, showcasing remarkable classification accuracy and data synthesis to bolster model training. The study acknowledges the limited research effort in investigating multimodal images related to breast cancer using deep learning techniques.

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TensorFlow

FEBRUARY 6, 2024

Discrete mathematics and computer science have a long history of formalizing such networks them as graphs , consisting of nodes arbitrarily connected by edges in various irregular ways. 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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Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? In this article, we’ll look at the state of the traditional machine learning landscape concerning modern generative AI innovations. What is Traditional Machine Learning?

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Unite.AI

AUGUST 28, 2024

Deep Instinct is a cybersecurity company that applies deep learning to cybersecurity. As I learned about the possibilities of predictive prevention technology, I quickly realized that Deep Instinct was the real deal and doing something unique. ML is unfit for the task. He holds a B.Sc Not all AI is equal.

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Marktechpost

MAY 2, 2024

Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Thus, while MLPs remain crucial, there’s ongoing exploration for more effective nonlinear regressors in neural network design.

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Marktechpost

JANUARY 24, 2024

With the growth of Deep learning, it is used in many fields, including data mining and natural language processing. However, deep neural networks are inaccurate and can produce unreliable outcomes. It can improve deep neural networks’ reliability in inverse imaging issues.

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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

MARCH 26, 2024

Deep learning models have recently gained significant popularity in the Artificial Intelligence community. In order to address these challenges, a team of researchers has introduced DomainLab, a modular Python package for domain generalization in deep learning. If you like our work, you will love our newsletter.

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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

JUNE 30, 2024

Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation. On the other hand, SSMs are a promising approach for modeling sequential data in deep learning.

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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

NOVEMBER 25, 2023

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. Traditional neural networks lack the elegance of the brain’s processing mechanisms.

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Towards AI

SEPTEMBER 10, 2023

What I’ve learned from the most popular DL course Photo by Sincerely Media on Unsplash I’ve recently finished the Practical Deep Learning Course from Fast.AI. I’ve passed many ML courses before, so that I can compare. So you definitely can trust his expertise in Machine Learning and Deep Learning.

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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

JUNE 16, 2024

Topological Deep Learning (TDL) advances beyond traditional GNNs by modeling complex multi-way relationships, unlike GNNs that only capture pairwise interactions. This capability is critical for understanding complex systems like social networks and protein interactions. PyG and DGL cater to both GDL and general graph learning.

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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

MARCH 23, 2024

AI researchers are taking the game to a new level with geometric deep learning. At its core, TacticAI relies on a cutting-edge geometric deep learning pipeline to turn raw football data into structured inputs for AI models to understand. Check out the Paper and Blog. Also, don’t forget to follow us on Twitter.

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Marktechpost

MARCH 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. PyTorch PyTorch is a widely used open-source machine learning library based on the Torch library.

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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). Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

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Marktechpost

JUNE 9, 2024

Deep learning methods excel in detecting cardiovascular diseases from ECGs, matching or surpassing the diagnostic performance of healthcare professionals. Researchers at the Institute of Biomedical Engineering, TU Dresden, developed a deep learning architecture, xECGArch, for interpretable ECG analysis.

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Marktechpost

NOVEMBER 8, 2023

The recent developments in the fields of Artificial Intelligence and Machine Learning have made everyone’s lives easier. With their incredible capabilities, AI and ML are diving into every industry and solving problems. Overconfidence is a prevalent issue, particularly in the context of deep neural networks.

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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. Check out the Paper and Reference Article. All Credit For This Research Goes To the Researchers on This Project.

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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. Check out the Paper.

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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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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

MAY 5, 2024

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. This layer adapts its kernel using a conditioning neural network, significantly enhancing Orchid’s ability to filter long sequences effectively.

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Marktechpost

JANUARY 10, 2024

The first perspective discussed using deep learning to pre-process intensity measurements before phase recovery. In the second perspective, the researchers focused on the Deep-learning-post-processing technique for phase recovery. They used deep learning during the phase recovery process.

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Marktechpost

SEPTEMBER 3, 2023

Credit assignment in neural networks for correcting global output mistakes has been determined using many synaptic plasticity rules in natural neural networks. Methods of biological neuromodulation have inspired several plasticity algorithms in models of neural networks.

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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. Currently, a major challenge faced in the theory of deep learning is the issue of scalability.

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Unite.AI

MARCH 15, 2024

Nevertheless, addressing the cost-effectiveness of ML models for business is something companies have to do now. For businesses beyond the realms of big tech, developing cost-efficient ML models is more than just a business process — it's a vital survival strategy. Challenging Nvidia, with its nearly $1.5

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Marktechpost

OCTOBER 18, 2023

One of the biggest challenges in Machine Learning has always been to train and use neural networks efficiently. In recent research, a team of researchers has introduced a deep learning compiler specifically made for neural network training.

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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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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.

Neural Network 116

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 116

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.

Neural Network 144

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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. Check out the Paper.

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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. This unsupervised neural network reconstruction method retains a significant speedup of previous deep learning-based methods while improving the quality simultaneously.

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