Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. حسب Gursimran Singh (575288)

الموضوعات:

Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. حسب Gursimran Singh (575288)

الموضوعات:

A typical BCI system depicting the flow and processing of EEG data. حسب Gursimran Singh (575288)

الموضوعات:

Visualization of the RITSS algorithm. حسب Julian Hecker (758791)

Test results of the benchmark function, the dimension fixed to one hundred. حسب Rahila Hafiz (18137254)

Bayes estimator and posterior risk under different loss functions. حسب Wanting Wang (8612121)

Friedman ranks of tested methods on CEC2019 benchmark functions. حسب Nebojsa Bacanin (13932723)

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

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.…"

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes حسب Olivier Collier (7444844)

Equivalence of loss functions and effects of regularization. حسب Jonathan Vacher (17041542)

الموضوعات: "…produced successful algorithms…"

Convergence curves of the IMGO and comparison algorithms on functions <i>f</i>1−<i>f</i>7. حسب Ying Li (38224)

Parameter settings of IWOA and other selected algorithms. حسب Wei Liu (20030)

الموضوعات:

Logarithmic mean convergence curves of different algorithms. حسب Wei Liu (20030)

الموضوعات:

Parameter settings of IWOAIKFS and other selected algorithms. حسب Wei Liu (20030)

الموضوعات:

Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots" حسب Neal Munyebvu (19690954)

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