|
gptkb:Generative_Pre-trained_Transformer_1
|
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
|
|
gptkb:generalized_inverse_Gaussian_distribution
|
f(x) = (psi/chi)^(lambda/2) / (2 K_lambda(sqrt(chi psi))) x^(lambda-1) exp(-(chi/x + psi x)/2)
|
|
gptkb:Multinomial_Naive_Bayes
|
gptkb:A_Comparison_of_Event_Models_for_Naive_Bayes_Text_Classification
|
|
gptkb:gamma_distribution_(with_shape_n)
|
lambda^n x^{n-1} e^{-lambda x} / Gamma(n)
|
|
gptkb:chi-squared_distribution_(with_2_degrees_of_freedom)
|
f(x) = (1/2) * exp(-x/2) for x > 0
|
|
gptkb:Nook_GlowLight_3
|
Yes
|
|
gptkb:T5-Small
|
gptkb:Exploring_the_Limits_of_Transfer_Learning_with_a_Unified_Text-to-Text_Transformer
|
|
gptkb:Weibull_distribution
|
f(x; k, λ) = (k/λ) (x/λ)^{k-1} e^{-(x/λ)^k} for x ≥ 0
|
|
gptkb:Pearson_Type_VI_distribution
|
f(x; α1, α2, β) = (x/β)^{α1-1} / [β B(α1, α2) (1 + x/β)^{α1+α2}] for x > 0
|
|
gptkb:Onyx_Boox_Max_Lumi
|
yes
|
|
gptkb:beta_distribution
|
x^(alpha-1) * (1-x)^(beta-1) / B(alpha, beta)
|
|
gptkb:Pearson_Type_0
|
f(x) = (1/(σ√(2π))) * exp(- (x-μ)^2 / (2σ^2))
|
|
gptkb:Inverse_chi-squared_distribution
|
f(x; ν) = (2^{−ν/2}/Γ(ν/2)) x^{−(ν/2)−1} exp(−1/(2x))
|
|
gptkb:Onyx_Boox_Max_4
|
yes
|
|
gptkb:Pearson_Type_V_distribution
|
f(x) = (b^a / Γ(a)) x^{-(a+1)} e^{-b/x}, x > 0
|
|
gptkb:PageWide_XL_8000
|
yes
|
|
gptkb:arXiv:1411.4028
|
https://arxiv.org/pdf/1411.4028.pdf
|
|
gptkb:Chi-squared_distribution
|
f(x; k) = (1/(2^{k/2} Γ(k/2))) x^{k/2-1} e^{-x/2}
|
|
gptkb:Pearson_Type_0_distribution
|
normal distribution PDF
|
|
gptkb:standard_multivariate_normal_distribution
|
(2π)^(-n/2) exp(-1/2 x^T x)
|