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Drones and AI roped in to help rid Malta of invasive plants

The new technology will run its first tests at Għajn Tuffieħa Bay

Malta , Convolutional-neural-networks-cnns , University-of-malta , Malta-information-technology-agency , Thursday-ambjent-malta , Convolutional-neural-networks , Minister-miriam-dalli , Ambjent-malta ,

Seeing the magic of artificial intelligence applications in ophthalmology

Seeing the magic of artificial intelligence applications in ophthalmology
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Convolutional-neural-networks-cnns , Artificial-intelligence , Neural-networks ,

Amplifying Additive Manufacturing with Artificial Intelligence - 3DPrint.com | The Voice of 3D Printing / Additive Manufacturing

Amplifying Additive Manufacturing with Artificial Intelligence - 3DPrint.com | The Voice of 3D Printing / Additive Manufacturing
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China , Chinese , Convolutional-neural-networks-cnns , Artificial-intelligence , Quality-control , Convolutional-neural-networks , Process-refinement , Natural-language-processing , Additive-manufacturing , Generative-design-hackathon , Moving-space-hackathon

"Going Deeper with Recursive Convolutional Layers" by Johan Chagnon, Markus Hagenbuchner et al.

The development of Convolutional Neural Networks (CNNs) trends towards models with an ever growing number of Convolutional Layers (CLs) and increases the number of trainable parameters significantly. Such models are sensitive to these structural parameters, which implies that large models have to be carefully tuned using hyperparameter optimisation, a process that can be very time consuming. In this paper, we study the usage of Recursive Convolutional Layers (RCLs), a module relying on an algebraic feedback loop wrapped around a CL, which can replace any CL in CNNs. Using three publicly available datasets, CIFAR10, CIFAR100 and SVHN, and a simple model comprised of 4 RCLs, we compare its performances with those obtained by its feedforward counterpart, and exhibit some core properties and use-cases of RCLs. In particular, we show that RCLs can lead to models of better performances, and that reducing the number of modules from four to one lead to a decrease in accuracy of 3.5% on average for models using RCLs, compared to 23% using CLs. Hence, the resulting architecture is much more robust to the addition or the removal of layers. We conclude by relating the effects obtained using additional CLs with those obtained using additional recursion on RCLs, which provides incentives that the latter can simulate an increase of depth but with no extra cost of parameters. Such results point to the potential benefits of either selectively or replacing all CLs by RCLs, in most recently introduced CNNs.

Convolutional-neural-networks-cnns , Convolutional-neural-networks , Convolutional-layers , Recursive-convolutional-layers , Convolutional-neural-network , Ynamic-depth , Mage-classification , Ecursive-neural-network ,

Demystifying Deep Learning Algorithms

Demystifying Deep Learning Algorithms
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Paul-youngblood , Convolutional-neural-networks-cnns , Generative-adversarial-networks-gans , Recurrent-neural-networks-rnns , Artificial-neural-networks-anns , Adversarial-networks , Neural-networks , Connected-networks ,

From Pixels to Patterns: Image Analysis Techniques

In the age of visual information, understanding images is no longer just the domain of the human eye. With rapid advancements in technology, image analysis

Convolutional-neural-networks-cnns , Gray-level-co-occurrence-matrix , Convolutional-neural-networks ,

Machine Learning vs Deep Learning vs Foundation Models

Learn more about the relationship between Machine Learning vs Deep Learning vs Foundation Models and how they effect AI models working as a

Recurrent-neural-networks-rnns , Convolutional-neural-networks-cnns , Google , Foundation-models , Generative-adversarial-networks-gans , Machine-learning , Deep-learning , Artificial-intelligence , Neural-networks , Adversarial-networks , Task-learning

Efficient FIR filtering with Bit Layer Multiply Accumulator

Bit Layer Multiplier Accumulator (BLMAC) is an efficient method to perform dot products without multiplications that exploits the bit level sparsity of the weights. A total of 1,980,000 low, high, band pass and band stop type I FIR filters were generated by systematically sweeping through the cut off frequencies and by varying the number of taps from 55 to 255.

Randy-yates , Vincenzo-liguori , Artix-ultrascale , Kintex-ultrascale , Convolutional-neural-networks-cnns , Connections-for-efficient-neural-networks , Cnn , Ocean-logic-pty-ltd , Ocean-logic-pty , Layer-multiplier-accumulator , Convolutional-neural-networks , Layer-multiply

Unlocking the Power of AI: A Deep Dive into Advanced Technology

Have you ever wondered how AI, once a mere concept in science fiction, has now become an indispensable part of our daily lives? From voice-activated virtual assistants to autonomous vehicles, the realm of artificial intelligence has expanded beyond imagination What makes AI advanced?We'll look at the heart of the matter in a few sentences. It

Boston , Massachusetts , United-states , United-kingdom , India , Marvin-minsky , Alan-turing , John-mccarthy , Virtual-health-assistants , Google-health , Netflix , United-kingdom-national-health-service

Watch AI learn to walk using Deep Reinforcement Learning (DRL)

In this unique experiment you can see how AI learns to walk using deeper reinforcement learning. Testing different methods and adapting

Recurrent-neural-networks-rnns , Convolutional-neural-networks-cnns , Reinforcement-learning , Convolutional-neural-networks , Recurrent-neural-networks ,