流行模型是理解传染病的强大工具。但是，随着它们的大小和复杂性的增加，它们可以迅速在计算上棘手。建模方法的最新进展表明，替代模型可用于模拟具有高维参数空间的复杂流行模型。我们表明，深层序列到序列（SEQ2SEQ）模型可以作为具有基于序列模型参数的复杂流行病模型的准确替代物，从而有效地复制了季节性和长期传播动力学。一旦受过培训，我们的代理人可以预测场景比原始模型快几千倍，从而使其非常适合策略探索。我们证明，用博学的模拟器代替传统的流行模型有助于强大的贝叶斯推断。

在本文中，我们在卷积神经网络（CNNS）的不断扩大性文献中介绍了一个新问题。虽然以前的工作侧重于如何在视觉解释CNNS的问题上，但我们问我们关心解释的是什么，即哪些层和神经元值得我们关注？由于巨大的现代深度学习网络架构，自动化，定量方法需要对神经元的相对重要性进行排名，以便为此问题提供答案。我们提出了一种新的统计方法，用于在网络的任何卷积层中排名隐藏的神经元。我们将重要性定义为激活映射与类分数之间的最大相关性。我们提供了不同的方式，其中该方法可用于可视化与Mnist和Imagenet的目的，并显示我们对街道级图像的空气污染预测方法的真实应用。

贝叶斯正交（BQ）是一种解决贝叶斯方式中数值集成问题的方法，允许用户量化其对解决方案的不确定性。 BQ的标准方法基于Intains的高斯过程（GP）近似。结果，BQ本质上仅限于可以以有效的方式完成GP近似的情况，因此通常禁止非常高维或非平滑的目标功能。本文提出使用基于贝叶斯添加剂回归树（BART）前锋的新的贝叶斯数值集成算法来解决这个问题，我们调用Bart-Int。 BART Priors易于调整，适合不连续的功能。我们证明它们在顺序设计环境中，它们也会自然地借给自己，并且可以在各种设置中获得显式收敛速率。这种新方法的优点和缺点在包括Genz功能的一组基准测试和贝叶斯调查设计问题上突出显示。

随机过程提供了数学上优雅的方式模型复杂数据。从理论上讲，它们为可以编码广泛有趣的假设的功能类提供了灵活的先验。但是，实际上，难以通过优化或边缘化来有效推断，这一问题进一步加剧了大数据和高维输入空间。我们提出了一种新颖的变性自动编码器（VAE），称为先前的编码变量自动编码器（$ \ pi $ vae）。 $ \ pi $ vae是有限的交换且Kolmogorov一致的，因此是一个连续的随机过程。我们使用$ \ pi $ vae学习功能类的低维嵌入。我们表明，我们的框架可以准确地学习表达功能类，例如高斯流程，也可以学习函数的属性以启用统计推断（例如log高斯过程的积分）。对于流行的任务，例如空间插值，$ \ pi $ vae在准确性和计算效率方面都达到了最先进的性能。也许最有用的是，我们证明了所学的低维独立分布的潜在空间表示提供了一种优雅，可扩展的方法，可以在概率编程语言（例如Stan）中对随机过程进行贝叶斯推断。

Many business workflows require extracting important fields from form-like documents (e.g. bank statements, bills of lading, purchase orders, etc.). Recent techniques for automating this task work well only when trained with large datasets. In this work we propose a novel data augmentation technique to improve performance when training data is scarce, e.g. 10-250 documents. Our technique, which we call FieldSwap, works by swapping out the key phrases of a source field with the key phrases of a target field to generate new synthetic examples of the target field for use in training. We demonstrate that this approach can yield 1-7 F1 point improvements in extraction performance.

Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-modal retrieval. In this work, we establish a connection between multimodal representation learning and multiple instance learning. Based on this connection, we propose a generic framework for constructing permutation-invariant score functions with many existing multimodal representation learning approaches as special cases. Furthermore, we use the framework to derive a novel contrastive learning approach and demonstrate that our method achieves state-of-the-art results on a number of downstream tasks.

Communication enables agents to cooperate to achieve their goals. Learning when to communicate, i.e., sparse (in time) communication, and whom to message is particularly important when bandwidth is limited. Recent work in learning sparse individualized communication, however, suffers from high variance during training, where decreasing communication comes at the cost of decreased reward, particularly in cooperative tasks. We use the information bottleneck to reframe sparsity as a representation learning problem, which we show naturally enables lossless sparse communication at lower budgets than prior art. In this paper, we propose a method for true lossless sparsity in communication via Information Maximizing Gated Sparse Multi-Agent Communication (IMGS-MAC). Our model uses two individualized regularization objectives, an information maximization autoencoder and sparse communication loss, to create informative and sparse communication. We evaluate the learned communication `language' through direct causal analysis of messages in non-sparse runs to determine the range of lossless sparse budgets, which allow zero-shot sparsity, and the range of sparse budgets that will inquire a reward loss, which is minimized by our learned gating function with few-shot sparsity. To demonstrate the efficacy of our results, we experiment in cooperative multi-agent tasks where communication is essential for success. We evaluate our model with both continuous and discrete messages. We focus our analysis on a variety of ablations to show the effect of message representations, including their properties, and lossless performance of our model.

Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activities. Avoiding contrails and adjusting flight routes could be an inexpensive and effective way to reduce their impact. An accurate, automated, and reliable detection algorithm is required to develop and evaluate contrail avoidance strategies. Advancement in contrail detection has been severely limited due to several factors, primarily due to a lack of quality-labeled data. Recently, proposed a large human-labeled Landsat-8 contrails dataset. Each contrail is carefully labeled with various inputs in various scenes of Landsat-8 satellite imagery. In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones. This work is the first to apply state-of-the-art segmentation techniques to detect contrails in low-orbit satellite imagery. Our work can also be used as an open benchmark for contrail segmentation and is publicly available.

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected individuals, potential adversaries could also exploit these recourses to compromise privacy. In this work, we make the first attempt at investigating if and how an adversary can leverage recourses to infer private information about the underlying model's training data. To this end, we propose a series of novel membership inference attacks which leverage algorithmic recourse. More specifically, we extend the prior literature on membership inference attacks to the recourse setting by leveraging the distances between data instances and their corresponding counterfactuals output by state-of-the-art recourse methods. Extensive experimentation with real world and synthetic datasets demonstrates significant privacy leakage through recourses. Our work establishes unintended privacy leakage as an important risk in the widespread adoption of recourse methods.

Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid over-confident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions.