在立体声视觉中，自相似或平淡的区域可能使得很难匹配两个图像之间的补丁。基于主动立体声的方法通过在场景上投射伪随机模式来减轻此问题，以便可以在没有歧义的情况下识别图像对的每个贴片。但是，投影模式显着改变了图像的外观。如果这种模式充当对抗性噪声的一种形式，则可能对基于深度学习的方法的性能产生负面影响，这现在是密集立体声视觉的事实上的标准。在本文中，我们提出了Active-Passive Simstereo数据集和相应的基准测试，以评估立体声匹配算法的被动立体声和活动立体声图像之间的性能差距。使用提出的基准测试和额外的消融研究，我们表明特征提取和匹配的模块选择了20个选择的基于深度学习的立体声匹配方法，可以推广到主动立体声，没有问题。但是，由于二十个体系结构（ACVNet，Cascadestereo和Stereonet）中三个的差异细化模块由于对输入图像的外观的依赖而受到主动立体声模式的负面影响。

立体声视觉最新发展的主要重点是如何在被动立体声视觉中获得准确的密集差异图。与被动立体声相比，主动视觉系统可以更准确地估计致密差异。但是，子像素准确的差异估计仍然是一个空的问题，几乎没有得到关注。在本文中，我们提出了一种新的学习策略，以训练神经网络，以估计半密集的主动立体声视觉的高质量子像素差异图。关键的见解是，如果神经网络能够共同学习如何完善差异图，同时使像素不足以纠正差异估计值，那么它们的准确性就可以翻倍。我们的方法基于贝叶斯建模，在该模型中，经过验证和无效的像素由它们的随机属性定义，从而使模型可以学习如何自行选择哪些像素值得关注。使用主动立体声数据集（例如Active-Passive Simstereo），我们证明了所提出的方法优于当前最新的活动立体声模型。我们还证明，所提出的方法与米德尔伯里数据集上的最新被动立体声模型进行了优惠比较。

基于成本的图像补丁匹配是计算机视觉，摄影测量和遥感的各种技术的核心。当需要在源图像和目标图像中的参考补丁之间的子像素视差时，必须内插的成本函数或目标图像。虽然基于成本的插值是最容易实现的，但是多个工程已经表明，基于图像的插值可以提高子像素匹配的准确性，但通常以昂贵的搜索过程的成本。然而，这是有问题的，特别是对于诸如立体声匹配或光学流量计算的非常计算密集型应用。在本文中，我们示出了用于一维匹配的壳体差异计算的闭合形式公式，例如，在搜索空间的纠正立体声图像的情况下，在使用标准的NCC，SSD和SAD时存在一个维度。成本函数。然后，我们展示了如何将所提出的公式概括为高维搜索空间的情况，这是未经化的立体声匹配和光学流量提取所必需的。我们还将结果与传统的成本卷插值公式以及最先进的成本的细化方法进行比较，并表明所提出的公式对基于最先进的成本提供了较小的改进在一维搜索空间的情况下的方法，以及搜索空间是二维时的显着改进。

现代深度学习方法构成了令人难以置信的强大工具，以解决无数的挑战问题。然而，由于深度学习方法作为黑匣子运作，因此与其预测相关的不确定性往往是挑战量化。贝叶斯统计数据提供了一种形式主义来理解和量化与深度神经网络预测相关的不确定性。本教程概述了相关文献和完整的工具集，用于设计，实施，列车，使用和评估贝叶斯神经网络，即使用贝叶斯方法培训的随机人工神经网络。

Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This paradigm suffers from two major limitations, over-squashing and poor long-range dependencies, that can be solved using global attention but significantly increases the computational cost to quadratic complexity. In this work, we propose an alternative approach to overcome these structural limitations by leveraging the ViT/MLP-Mixer architectures introduced in computer vision. We introduce a new class of GNNs, called Graph MLP-Mixer, that holds three key properties. First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on the Long Range Graph Benchmark (LRGB) and the TreeNeighbourMatch datasets. Second, they offer better speed and memory efficiency with a complexity linear to the number of nodes and edges, surpassing the related Graph Transformer and expressive GNN models. Third, they show high expressivity in terms of graph isomorphism as they can distinguish at least 3-WL non-isomorphic graphs. We test our architecture on 4 simulated datasets and 7 real-world benchmarks, and show highly competitive results on all of them.

Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference-a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS).

Information on the grass growth over a year is essential for some models simulating the use of this resource to feed animals on pasture or at barn with hay or grass silage. Unfortunately, this information is rarely available. The challenge is to reconstruct grass growth from two sources of information: usual daily climate data (rainfall, radiation, etc.) and cumulative growth over the year. We have to be able to capture the effect of seasonal climatic events which are known to distort the growth curve within the year. In this paper, we formulate this challenge as a problem of disaggregating the cumulative growth into a time series. To address this problem, our method applies time series forecasting using climate information and grass growth from previous time steps. Several alternatives of the method are proposed and compared experimentally using a database generated from a grassland process-based model. The results show that our method can accurately reconstruct the time series, independently of the use of the cumulative growth information.

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifically designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, by countries or by revenues buckets. We also compare our results to those of other providers and find our estimates to be more accurate. Thanks to the proposed explainability tools using Shapley values, our model is fully interpretable, the user being able to understand which factors split explain the GHG emissions for each particular company.

Robots are traditionally bounded by a fixed embodiment during their operational lifetime, which limits their ability to adapt to their surroundings. Co-optimizing control and morphology of a robot, however, is often inefficient due to the complex interplay between the controller and morphology. In this paper, we propose a learning-based control method that can inherently take morphology into consideration such that once the control policy is trained in the simulator, it can be easily deployed to robots with different embodiments in the real world. In particular, we present the Embodiment-aware Transformer (EAT), an architecture that casts this control problem as conditional sequence modeling. EAT outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired robot embodiment, past states, and actions, our EAT model can generate future actions that best fit the current robot embodiment. Experimental results show that EAT can outperform all other alternatives in embodiment-varying tasks, and succeed in an example of real-world evolution tasks: stepping down a stair through updating the morphology alone. We hope that EAT will inspire a new push toward real-world evolution across many domains, where algorithms like EAT can blaze a trail by bridging the field of evolutionary robotics and big data sequence modeling.

Dependency hell is a well-known pain point in the development of large software projects and machine learning (ML) code bases are not immune from it. In fact, ML applications suffer from an additional form, namely, "data source dependency hell". This term refers to the central role played by data and its unique quirks that often lead to unexpected failures of ML models which cannot be explained by code changes. In this paper, we present an automated dependency mapping framework that allows MLOps engineers to monitor the whole dependency map of their models in a fast paced engineering environment and thus mitigate ahead of time the consequences of any data source changes (e.g., re-train model, ignore data, set default data etc.). Our system is based on a unified and generic approach, employing techniques from static analysis, from which data sources can be identified reliably for any type of dependency on a wide range of source languages and artefacts. The dependency mapping framework is exposed as a REST web API where the only input is the path to the Git repository hosting the code base. Currently used by MLOps engineers at Microsoft, we expect such dependency map APIs to be adopted more widely by MLOps engineers in the future.