Unverified50% confidenceFactExact time
The paper proposes a discriminative reformulation that uses log-odds as the basis for supervised representations, replacing the traditional conditional log-likelihood.
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10/9/2026
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UnverifiedLogistic regression assumes that the log-odds is a linear combination of features, maps to probability space via the sigmoid function, and uses cross-entropy (log-likelihood) as the objective function.67% similarUnverifiedNormalizing flows exactly compute the log-likelihood of data via the change-of-variables formula: log p(x) = log p(f(x)) + log |det(∂f/∂x)|.62% similarUnverifiedThe paper obtained explicit polynomial convergence rates with respect to the number of iteration steps N, step size h, and problem dimension d.59% similarUnverifiedlogit函数定义为log(p/(1-p))，将概率值从[0,1]映射到(-∞,+∞)，是广义线性模型中处理二分类因变量的核心技术58% similarUnverifiedThe core contribution of the paper is the formalization of a broad class of approximation schemes, named "adaptive representations."57% similar
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