Fitness functions map large combinatorial spaces of biological sequences to
properties of interest. Inferring these multimodal functions from experimental
data is a central task in modern protein engineering. Global epistasis models
are an effective and physically-grounded class of models for estimating fitness
functions from observed data. These models assume that a sparse latent function
is transformed by a monotonic nonlinearity to emit measurable fitness. Here we
demonstrate that minimizing contrastive loss functions, such as the
Bradley-Terry loss, is a simple and flexible technique for extracting the
sparse latent function implied by global epistasis. We argue by way of a
fitness-epistasis uncertainty principle that the nonlinearities in global
epistasis models can produce observed fitness functions that do not admit
sparse representations, and thus may be inefficient to learn from observations
when using a Mean Squared Error (MSE) loss (a common practice). We show that
contrastive losses are able to accurately estimate a ranking function from
limited data even in regimes where MSE is ineffective. We validate the
practical utility of this insight by showing contrastive loss functions result
in consistently improved performance on benchmark tasks.

通过对全局阴性相互作用模型使用对比损失函数技术，文章提出并验证该技术可以在有限的数据量下，精确地估计排名函数，并具有在常规均方误差损失函数无效的情况下提高性能的实际效用。

对比损失作为全局上表观互作的广义模型

Contrastive losses as generalized models of global epistasis

Generative Adversarial Networks (GANs) are a machine learning approach
capable of generating novel example outputs across a space of provided training
examples. Procedural Content Generation (PCG) of levels for video games could
benefit from such models, especially for games where there is a pre-existing
corpus of levels to emulate. This paper trains a GAN to generate levels for
Super Mario Bros using a level from the Video Game Level Corpus. The approach
successfully generates a variety of levels similar to one in the original
corpus, but is further improved by application of the Covariance Matrix
Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions
are used to discover levels within the latent space of the GAN that maximize
desired properties. Simple static properties are optimized, such as a given
distribution of tile types. Additionally, the champion A* agent from the 2009
Mario AI competition is used to assess whether a level is playable, and how
many jumping actions are required to beat it. These fitness functions allow for
the discovery of levels that exist within the space of examples designed by
experts, and also guide the search towards levels that fulfill one or more
specified objectives.

本论文使用对抗生成网络 (GANs) 和协方差矩阵自适应进化策略 (CMA-ES) 生成了大量类似原始语料库中的 Super Mario Bros 的电子游戏层级，并使用各种适应性函数发现了专家所设计示例空间内存在的层级，并引导搜索满足一个或多个指定目标的层级。