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gptkb:CityPersons_Dataset
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https://arxiv.org/abs/1702.05693
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gptkb:Inverse_chi-squared_distribution
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f(x; ν) = (2^{−ν/2}/Γ(ν/2)) x^{−(ν/2)−1} exp(−1/(2x))
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gptkb:Shap-E_(2023)
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https://arxiv.org/abs/2305.02463
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gptkb:exponential_distribution
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lambda * exp(-lambda * x)
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gptkb:Multinomial_naive_Bayes
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gptkb:A_Comparison_of_Event_Models_for_Naive_Bayes_Text_Classification
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gptkb:Fisher–Snedecor_distribution
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f(x; d1, d2) = [d1^{d1/2} d2^{d2/2} x^{(d1/2)-1}] / [B(d1/2, d2/2) (d1 x + d2)^{(d1+d2)/2}]
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gptkb:univariate_t-distribution
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f(x) = Γ((ν+1)/2) / (√(νπ) Γ(ν/2)) (1 + x²/ν)^(-(ν+1)/2)
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gptkb:Multinomial_Naive_Bayes
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gptkb:A_Comparison_of_Event_Models_for_Naive_Bayes_Text_Classification
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gptkb:chi-squared_distribution
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(1/(2^{k/2}Γ(k/2))) x^{(k/2)-1} e^{-x/2}
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gptkb:Pearson_type_VII_distribution
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has heavy tails
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gptkb:YOLOX
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https://arxiv.org/abs/2107.08430
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gptkb:triangular_distribution
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piecewise linear
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gptkb:Onyx_Boox_Tab_X
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yes
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gptkb:Noncentral_chi-squared_distribution
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Involves modified Bessel function
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gptkb:chi_distribution
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(1/2^{k/2-1} Gamma(k/2)) x^{k-1} e^{-x^2/2}
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gptkb:Normal_distribution_(standard_parameterization)
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f(x) = (1/(σ√(2π))) * exp(- (x-μ)^2 / (2σ^2))
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gptkb:DeBERTa-Large
|
https://arxiv.org/abs/2006.03654
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gptkb:DINOv2
|
https://arxiv.org/abs/2304.07193
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gptkb:Fréchet_distribution
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f(x; α, s, m) = (α/s) ((x-m)/s)^(-1-α) exp(-((x-m)/s)^(-α)) for x > m
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gptkb:T5-Small
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gptkb:Exploring_the_Limits_of_Transfer_Learning_with_a_Unified_Text-to-Text_Transformer
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