Q.ANT’s Post

We're thrilled to share that B-Yond continues to lead in innovative network diagnostics with our latest research acceptance at ACM. Our paper, "PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection," has been accepted at the prestigious SIGCOMM Workshop on Networks for AI Computing (NAIC)! This marks our second paper this year focused on cutting-edge techniques for network failure detection. Utilizing computer vision and convolutional neural networks (CNNs), PCAPVision transforms PCAP data into images, enabling rapid and accurate failure detection with minimal computational overhead—perfect for edge computing environments. #AGILITY #FailureDetection #5g #AI #Diagnostics 👉 Check the comments for more details!

Have a look at this article on “LASER BEAM SHAPING WITH DIFFRACTIVE NEURAL NETWORKS” by Paul Buske from RWTH Aachen University. It's in the latest edition of AILU's quality publication "The Laser User" Issue 112 (Spring 2024) out now. It is considered the best source of practical laser user information available anywhere. #ailu #laserassociation #laserusers #laserbeamshaping #aachenuniversity

We're thrilled to share that B-Yond continues to lead in innovative network diagnostics with our latest research acceptance at ACM. Our paper, "PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection," has been accepted at the prestigious SIGCOMM Workshop on Networks for AI Computing (NAIC)! This marks our second paper this year focused on cutting-edge techniques for network failure detection. Utilizing computer vision and convolutional neural networks (CNNs), PCAPVision transforms PCAP data into images, enabling rapid and accurate failure detection with minimal computational overhead—perfect for edge computing environments. #AGILITY#FailurePrediction #5G #AI #Diagnostics 👉Discover how to run diagnostics at scale:  https://lnkd.in/g2Jubxcu 

Check out this video to learn more about this exciting computational model: Liquid Neural Networks https://lnkd.in/gdXJMTQY In the realm of neuromorphic and cognitive computing, researchers have been exploring the concept of liquid neural networks. Inspired by the neural networks in the brain, liquid networks simulate the dynamics of interconnected neurons to enable complex information processing. By leveraging the principles of neural dynamics, these networks can help with tasks such as pattern recognition, classification, and decision-making. Liquid Networks should be very helpful in computer vision, and autonomous driving. Take a look at Liquid.AI https://www.liquid.ai/

For those who want to know what CogniGron, University of Groningen (FSE) is researching, this comprehensive overview on neural networks and neuromorphic computing by Brilliant (I’m not involved with them) provides a lot of detail and insight.

📌 Check the pinned comment for the full article link! 🔮🚀 Quantum Machine Learning: A Game-Changer in AI🌟Dr. Utpal Chakraborty (PhD) highlights the transformative potential of Quantum Machine Learning (QML) in revolutionizing AI. IntellAI By leveraging 🤖quantum mechanics, QML promises to overcome the limitations of classical machine learning, offering enhanced computational power and efficiency. From encoding data into qubits to applying advanced quantum algorithms, QML is set to tackle complex problems and unlock new possibilities in AI. 🌐#QuantumMachineLearning #AIRevolution #TechInnovation #QML #FutureOfAI #QuantumComputing #DataScience #MachineLearning #IntellAI

This week’s featured article is breaking boundaries in analog mixed-signal circuit design! Authors unveil a cutting-edge approach to optimize performance while ensuring robustness under changing conditions. Dive into how the multi-stage regression modeling, powered by artificial neural networks and variational autoencoders, streamlines design optimization and co-finds optimum points. https://lnkd.in/ef2vyD3z

I am glad to share the first article in my series on #QuantumMachineLearning. Starting with the basics, I aim to make this complex topic accessible and informative. Whether you are new to the field or looking to refresh your knowledge, I hope you will find this series helpful! Thank you INDUSTRIAL AUTOMATION MAGAZINE for publishing it! Article Link: https://lnkd.in/daXjhqpV #QuantumMachineLearning #QML #QuantumComputing #AI #MachineLearning

CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries 

Don't crown Transformer models as the kings of AI just yet! There's a new contender in town, and it's called the "Legendre Memory Unit." This fresh approach to neural networks comes from the Professor Chris Elliasmith's University of Waterloo and Applied Brain Research team. By rehabilitating the RNN architecture with Legendre Polynomial, the team has outperformed the Transformer architecture using a smaller network. Not only that, but the smaller models also mean cheaper and lower energy chips. Check out this video: https://lnkd.in/etAqv6_b. #AI #MachineLearning #NeuralNetworks #LegendreMemoryUnit #RNN #Transformers #AppliedBrainResearch #UniversityOfWaterloo