AP values of three loss functions on the three datasets, including four indicators: AP@50, AP@75, AP@M, and AP@L. حسب Xunqian Xu (21261899)

Algorithm timings on a laptop computer with Intel i9–9900K CPU, 32GB RAM and an NVIDIA RTX2080 GPU. حسب Adam L. Tyson (10892822)

Non-Iterative Linear Maximization Algorithm حسب Gerardo L. Febres (4455913)

"…</div><div><br></div><div>Search "A non-iterative method for optimizing linear functions in convex linearly constrained spaces" by Gerardo L. …"

^aTwo HIV-1 protease mutants bound to inhibitor darunavir (G48V: PDB ID 3cyw [81] & L90M: PDB ID 2f81 [82]). حسب Yuxi Long (11024307)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... حسب Hector Torres (11708207)

Ap values of different methods on the three datasets. AP values include four indicators: AP@50, AP@75, AP@M, and AP@L. حسب Xunqian Xu (21261899)

Functional validation of the metastasis-predictive genes inferred by PLUS from TCGA data. حسب Junyi Zhou (495644)

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. حسب Gursimran Singh (575288)

الموضوعات:

Cost functions implemented in Neuroptimus. حسب Máté Mohácsi (20469514)

Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. حسب Adam L. Tyson (10892822)

Studies with ML classification algorithms for distinguishing ASD and ADHD groups described in subsection 1.2. حسب Caroline L. Alves (14271413)

Functional analysis of the oral dysbiosis dataset using molecular function GO terms. حسب Ray Sajulga (5721518)

Distinct functional composition between age groups. حسب Elio L. Herzog (11576876)

C-code for calculation of the temporal correlation function dependent on time. حسب Aleksey Valkov (9251744)

Simulation results for average reward rate function using the UCB algorithm , where <i>A</i> = 100, <i>ℓ</i> = 20, <i>μ</i> = [0.75, …(×50), 0.5, …(×50)], and <i>T</i> = 1000, and . حسب Justin Zobel (241587)

"…<p>Simulation results for average reward rate function using the UCB algorithm , where <i>A</i> = 100, <i>ℓ</i> = 20, <i>μ</i> = [0.75, …(×50), 0.5, …(×50)], and <i>T</i> = 1000, and .…"

500 <i>ϕ</i> vectors learned from <i>ℓ</i>_1/2 thresholding. حسب Ilias Rentzeperis (10215602)

"…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. …"

Flowchart demonstrates the optimization procedure of the basic HFPSO method sit [51]. حسب Abdullah Khan (377292)

الموضوعات:

Optimum tuning of the dependent variables for various cases using the HFPSO technique (standard IEEE 30-bus test network). حسب Abdullah Khan (377292)

الموضوعات:

Optimum solutions and tuning of the dependent variables for various cases based on the PSO technique (standard IEEE 30-bus test network). حسب Abdullah Khan (377292)

الموضوعات:

Evaluation of the solutions gained for voltage stability enhancement (Standard IEEE 30-bus test system). حسب Abdullah Khan (377292)

الموضوعات: