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.

使用深度学习对胸部射线照相的自动分析具有巨大的潜力，可以增强患者疾病的临床诊断。但是，深度学习模型通常需要大量的带注释的数据来实现高性能 - 通常是医疗领域适应的障碍。在本文中，我们构建了一个利用放射学报告来通过有限的标记数据（少于1000个示例）来改善医学图像分类性能，以提高医学图像分类性能。具体而言，我们检查了捕获图像预告片，以学习以更少的例子进行训练的高质量医学图像表示。在对卷积编码器和变压器解码器进行联合预测之后，我们将学习的编码器转移到各种分类任务中。平均9多种病理学，我们发现我们的模型在标记培训数据受到限制时，比参见和内域监督的预处理的分类性能更高。

在医疗保健中采用机器学习模型需要最终用户对系统的信任。为他们的预测提供额外的支持证据，以促进通过。我们定义了一致的证据，既兼容，又充分涉及模型预测。我们提出了促进更一致的证据的模型不一致和常规方案的措施。我们在从胸部射线照片的水肿严重程度分级的背景下展示了我们的想法。我们经验证明，一致的模型在支持解释时提供竞争性能。

我们提出并通过在图像和文本的本地特征之间最大化互信息来提出并展示表示学习方法。这种方法的目标是通过利用描述图像中发现的自由文本中包含的丰富信息来学习有用的图像表示。我们的方法通过鼓励产生的表示展示了高局部互信息来训练图像和文本编码器。我们利用神经网络鉴别器的互信息估算的最新进展。我们认为，本地互信息的总和通常是全球相互信息的较低限制。我们在下游图像分类任务中的实验结果展示了使用本地特征进行图像文本表示学习的优势。

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.

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.

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。