Traditional multi-task learning architectures train a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer. A new type of multi-task learning within NLP homogenizes multi-task architectures as a shared encoder and language model decoder, which does surprisingly well across a range of diverse tasks. Does this new architecture suffer from task-conflicts that require specialized training algorithms? We study how certain factors in the shift towards text-to-text models affects multi-task conflict and negative transfer, finding that both directional conflict and transfer are surprisingly constant across architectures.

机器学习（ML）研究通常集中在模型上，而最突出的数据集已用于日常的ML任务，而不考虑这些数据集对基本问题的广度，困难和忠诚。忽略数据集的基本重要性已引起了重大问题，该问题涉及现实世界中的数据级联以及数据集驱动标准的模型质量饱和，并阻碍了研究的增长。为了解决此问题，我们提出Dataperf，这是用于评估ML数据集和数据集工作算法的基准软件包。我们打算启用“数据棘轮”，其中培训集将有助于评估相同问题的测试集，反之亦然。这种反馈驱动的策略将产生一个良性的循环，该循环将加速以数据为中心的AI。MLCommons协会将维护Dataperf。

本文介绍了Cerberus机器人系统系统，该系统赢得了DARPA Subterranean挑战最终活动。出席机器人自主权。由于其几何复杂性，降解的感知条件以及缺乏GPS支持，严峻的导航条件和拒绝通信，地下设置使自动操作变得特别要求。为了应对这一挑战，我们开发了Cerberus系统，该系统利用了腿部和飞行机器人的协同作用，再加上可靠的控制，尤其是为了克服危险的地形，多模式和多机器人感知，以在传感器退化，以及在传感器退化的条件下进行映射以及映射通过统一的探索路径计划和本地运动计划，反映机器人特定限制的弹性自主权。 Cerberus基于其探索各种地下环境及其高级指挥和控制的能力，表现出有效的探索，对感兴趣的对象的可靠检测以及准确的映射。在本文中，我们报告了DARPA地下挑战赛的初步奔跑和最终奖项的结果，并讨论了为社区带来利益的教训所面临的亮点和挑战。

具有数值节点特征和图形结构的图形神经网络（GNNS）作为输入显示出具有图形数据的各种监督学习任务的卓越性能。但是，GNN使用的数值节点特征通常是从大多数真实世界应用中的文本或表格（数字/分类）类型的原始数据中提取的。在大多数标准监督的学习设置中，使用IID（NON-GRAPH）数据的最佳模型不是简单的神经网络层，因此不容易被纳入GNN。在这里，我们提出了一个强大的堆叠框架，该框架将图形感知的传播与用于IID数据的任意模型融合在一起，这些模型是在多层中结合并堆叠的。我们的层面框架利用行李和堆叠策略来享受强有力的概括，从而有效地减轻了标签泄漏和过度拟合的方式。在各种具有表格/文本节点特征的图形数据集中，我们的方法相对于表格/文本和图形神经网络模型以及将两者结合的现有最新混合策略而获得了可比性或卓越的性能。

图像分类器通常在其测试设置精度上进行评分，但高精度可以屏蔽微妙类型的模型故障。我们发现高分卷积神经网络（CNNS）在流行的基准上表现出令人不安的病理，即使在没有语义突出特征的情况下，即使在没有语义突出特征的情况下也能够显示高精度。当模型提供没有突出的输入功能而无突出的频率决定时，我们说分类器已经过度解释了它的输入，找到了太多的课程 - 以对人类荒谬的模式。在这里，我们展示了在CiFar-10和Imagenet上培训的神经网络患有过度诠释，我们发现CIFAR-10上的模型即使在屏蔽95％的输入图像中，人类不能在剩余像素子集中辨别出突出的特征。我们介绍了批量梯度SIS，一种用于发现复杂数据集的足够输入子集的新方法，并使用此方法显示故事中的边界像素的充分性以进行培训和测试。虽然这些模式在现实世界部署中移植了潜在的模型脆弱性，但它们实际上是基准的有效统计模式，单独就足以实现高测试精度。与对手示例不同，过度解释依赖于未修改的图像像素。我们发现合奏和输入辍学可以帮助缓解过度诠释。

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.