Empirical cumulative distribution function of RMSE for four sources. حسب Nauman Ahmed (8086868)

Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic حسب NELSON L. DIAS (8078003)

"…With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. …"

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. حسب Devi Soorya Narayana Sasikumar (22331743)

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Occlusion finite state machine diagram. حسب Frank R. Ihmig (9016778)

Machine learning models. حسب Meili Liu (327309)

الموضوعات:

Accuracy comparison for individual cyber attack with various algorithms. حسب Parthiban Aravamudhan (15338781)

الموضوعات:

Recursive attention function and loss function change. حسب Wang Linfeng (17102068)

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

"…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…"

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

"…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…"

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

"…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…"

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

"…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…"

Comparison of predictive performance of machine learning algorithms on classifying genes as pro- or anti-longevity. حسب F. William Townes (3216846)

Pseudo live-stream of EEG data is processed through the flow chart: after channel selection, each epoch undergoes Kaiser windowing (length = 250), followed by re-referencing to the... حسب Gursimran Singh (575288)

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PyNoetic’s online mode in action. Data is streamed from an Emotiv EPOC headset. حسب Gursimran Singh (575288)

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Illustration of PyNoetic’s 2D and 3D simulation module with visual feedback. حسب Gursimran Singh (575288)

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PyNoetic’s unique pick-place configurable flowchart that offers a no-code option for non-programmers. حسب Gursimran Singh (575288)

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PyNoetic’s feature extraction module that supports time domain features, frequency domain features, time-frequency domain features, spatial features as well as Brain Connectivity m... حسب Gursimran Singh (575288)

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Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering. حسب Gursimran Singh (575288)

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List of Abbreviations حسب Gursimran Singh (575288)

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PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. حسب Gursimran Singh (575288)

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