Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Final assignment algorithm. by Negin Alisoltani (11949357)

Optimized algorithm framework diagram. by Xutao Liu (13006965)

Subjects:

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation by Gianmarc Grazioli (6752297)

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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. …”

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. …”

Flowchart of DAPF-RRT algorithm. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Performance comparison of different algorithms. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Synthetic Realness: Authenticity as Algorithm (Reality Drift Working Paper Series, 2025) by Reality Drift Working Papers Series 2020-2025 (22445446)

“…<p dir="ltr">This paper explores the concept of synthetic realness: how authenticity is increasingly engineered by algorithms until the line between real and fake becomes functionally irrelevant. …”

The image of the Aekley function. by Zhengyu Xu (8550660)

Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

Subjects:

The IALO algorithm solution flowchart. by Wenjing Wang (181404)

Subjects:

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

Subjects:

Benchmarking functions. by Guilin Yang (583364)

Subjects:

Functions and techniques of adolescent health Latin dance teaching system. by Xutao Liu (13006965)

Subjects:

Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …”

An Efficient Algorithm for Minimizing Multi Non-Smooth Component Functions by Minh Pham (2458399)

“…<p>Many problems in statistics and machine learning can be formulated as an optimization problem of a finite sum of nonsmooth convex functions. We propose an algorithm to minimize this type of objective functions based on the idea of alternating linearization. …”