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gptkb:Lévy_distribution
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f(x; μ, c) = sqrt(c / (2π)) * exp(-c / (2(x-μ))) / (x-μ)^{3/2}, x > μ
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gptkb:Landau_distribution
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no closed-form expression
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gptkb:Onyx_Boox_Tab_Ultra_Air_Pro_10
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yes
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gptkb:Weibull_distribution
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f(x; k, λ) = (k/λ) (x/λ)^{k-1} e^{-(x/λ)^k} for x ≥ 0
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gptkb:Inception_v1
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https://arxiv.org/abs/1409.4842
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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:Log-gamma_distribution
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f(x; μ, θ, k) = (1/Γ(k)θ^k) exp(k(x-μ)/θ - exp((x-μ)/θ))
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gptkb:DeepSeek-MoE
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https://arxiv.org/abs/2405.13237
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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: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:Nakagami_distribution
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(2m^m)/(Γ(m)Ω^m) x^{2m-1} exp(-m x^2/Ω), x ≥ 0
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gptkb:Pearson_Type_0
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f(x) = (1/(σ√(2π))) * exp(- (x-μ)^2 / (2σ^2))
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gptkb:arXiv:1602.07261
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https://arxiv.org/pdf/1602.07261.pdf
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gptkb:Uniform_distribution_(when_alpha=1,_beta=1)
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1 for x in [0,1]
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gptkb:Pearson_Type_VI_distribution
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f(x; α1, α2, β) = (x/β)^{α1-1} / [β B(α1, α2) (1 + x/β)^{α1+α2}] for x > 0
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gptkb:BERT_on_SQuAD
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gptkb:BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding
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gptkb:F-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:CityPersons_Dataset
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https://arxiv.org/abs/1702.05693
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gptkb:2004.05150
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https://arxiv.org/pdf/1706.03762.pdf
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gptkb:Pearson_Type_X_distribution
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f(x; α, β) = (x^{α-1} (1+x)^{-α-β}) / B(α, β)
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