Final assignment algorithm. by Negin Alisoltani (11949357)

Optimized algorithm framework diagram. by Xutao Liu (13006965)

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Two-stage object detection algorithm process. by Xutao Liu (13006965)

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On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform by Zhehong Ai (17937324)

The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri. by Burak Alakent (463512)

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Introduction<p>Internalizing disorders (depression, anxiety, somatic symptom disorder) are among the most common mental health conditions that can substantially reduce daily life function. Early adolescence is an important developmental stage for the increase in prevalence of internalizing disorders and understanding specific factors that predict their onset may be germane to intervention and prevention strategies.…”

Effective cluster radius from Ripley’s H-function for the NPC dataset. by Daniel F. Nino (9745709)

Flowchart of the denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm. by Tsutomu Gomi (7382183)

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Functional module diagram. by Fan Hongxia (21029673)

Subjects: “…artificial intelligence algorithms…”

Experimental environment. by Xutao Liu (13006965)

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Optimized system structure. by Xutao Liu (13006965)

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S1 Data - by Xutao Liu (13006965)

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Feature subset search results. by Xutao Liu (13006965)

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The basic framework of dual flow networks. by Xutao Liu (13006965)

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Image convolution calculation process. by Xutao Liu (13006965)

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Model hyperparameters used for the XGBoost model. by Aastha Vatsyayan (9981916)

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Primers used in site-directed mutagenesis. by Aastha Vatsyayan (9981916)

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datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

datasheet2_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.zip by Santiago Hernández-Orozco (5070209)

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. …”