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导读：本文是“数据拾光者”专栏的第七十九篇文章，这个系列将介绍在广告行业中自然语言处理和推荐系统实践。本篇主要调研了长短多兴趣相关的论文。 欢迎转载，转载请注明出处以及链接，更多关于自然语言处理、推荐系统优质内容请关注如下频道。 知乎专栏：数据拾光者 公众号：数据拾光. 问题中所提到的可微渲染和可微分编程的关系，简单来说（1）两者密不可分，不管是 Redner (MIT group), PSDR/DTRT (Our group), 还是 Mitsuba2（Wenzel group）的实际是实现上都依赖Automated differentiation, 但是 （2）可微渲染并不是简单的可微分编程，比如说为解决visibility的问题. There are copious exercises to help guide the reader in testing understanding. Ap微积分bc课程 limits and continuity differentiation
Definition and fundamental properties differentiation Composite, implicit, and inverse functions contextual applications of differentiation analytical applications of differentiation integration and accumulation of change differential equations applications of integration To efficiently solve the objective, we exploit two properties of linear classifiers Implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem
比如STEP2中考试的differential equation的内容，从basic differentiation formulae到chain rule、product (quotient) rule of differentiation到implicit differentiation，再到separating variable，又回到basic differentiation formulae, 又串联了logarithmic， exponential的运算法则，有的还夹杂着partial fraction integral. Implicit regularization in nonconvex statistical estimation Gradient descent converges linearly for phase retrieval, matrix completion and blind deconvolution (2017) neon2
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