即使在给定的物种中，单个大脑在解剖结构和功能组织中也有所不同。当试图从受试者组收集的神经影像数据中得出可概括的结论时，个体间的可变性是一个主要障碍。当前的共同注册程序依赖于有限的数据，从而导致非常粗糙的主体间比对。在这项工作中，我们提出了一种基于最佳运输的主体间比对的新方法，称为融合不平衡的Gromov Wasserstein（FUGW）。该方法根据其功能特征的相似性来对齐皮质表面，以响应各种刺激设置，同时惩罚了单个地形组织的大变形。我们证明了FUGW非常适合全脑车地标的对齐。不平衡的功能可以处理以下事实：功能区域的大小各不相同。我们的结果表明，FUGW的对准显着增加了独立功能数据的活动间相关性，并导致在组级别上更精确的映射。

We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for these quantities are crucial to mitigate the negative effects of wind farms' intermittent production on energy systems and markets. We use machine learning to combine outputs from numerical weather prediction models with local observations. The former provide valuable information on higher scales dynamics while the latter gives the model fresher and location-specific data. So as to make the results usable for practitioners, we focus on well-known methods which can handle a high volume of data. We study first variable selection using both a linear technique and a nonlinear one. Then we exploit these results to forecast wind speed and wind power still with an emphasis on linear models versus nonlinear ones. For the wind power prediction, we also compare the indirect approach (wind speed predictions passed through a power curve) and the indirect one (directly predict wind power).

本文介绍了一个新颖而通用的框架，以利用最佳运输工具来解决监督标记的图形预测的旗舰任务。我们将问题提出为融合Gromov-Wasserstein（FGW）损失的回归，并提出了一个依靠FGW Barycenter的预测模型，该模型的权重取决于输入。首先，我们基于内核脊回归引入了一个非参数估计量，该估计量得到了理论结果，例如一致性和过量风险绑定。接下来，我们提出了一个可解释的参数模型，其中Barycenter权重用神经网络建模，并进一步学习了FGW Barycenter的图形。数值实验表明了该方法的强度及其在模拟数据上标记的图形空间以及难以实现的代谢识别问题上插值的能力，在这种情况下，它几乎没有工程学才能达到非常好的性能。

比较图形等结构的对象是许多学习任务中涉及的基本操作。为此，基于最优传输（OT）的Gromov-Wasserstein（GW）距离已被证明可以成功处理相关对象的特定性质。更具体地说，通过节点连接关系，GW在图表上运行，视为特定空间上的概率测量。在OT的核心处是质量守恒的想法，这在两个被认为的图表中的所有节点之间施加了耦合。我们在本文中争辩说，这种财产可能对图形字典或分区学习等任务有害，我们通过提出新的半轻松的Gromov-Wasserstein发散来放松它。除了立即计算福利之外，我们讨论其属性，并表明它可以导致有效的图表字典学习算法。我们经验展示其对图形上的复杂任务的相关性，例如分区，聚类和完成。

最佳运输（OT）理论下潜许多新兴机器学习（ML）方法现在解决了各种任务，例如生成建模，转移学习和信息检索。然而，这些后者通常会在传统的OT设置上具有两个分布，同时留下更一般的多边缘OT配方，稍微探索。在本文中，我们研究了多边缘OT（MMOT）问题，并通过促进关于耦合的结构信息，统一其伞下的几种流行的OT方法。我们表明将这种结构信息结合到MMOT中，在允许我们在数值上解决它的不同凸（DC）编程问题的实例。尽管后一级的计算成本高，但DC优化提供的解决方案通常与使用当前采用的优化方案获得的解决方案一样定性。

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn $\epsilon$-optimal strategies in a zero-sum IIG through self-play with trajectory feedback. We give a problem-independent lower bound $\mathcal{O}(H(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ on the required number of realizations to learn these strategies with high probability, where $H$ is the length of the game, $A_{\mathcal{X}}$ and $B_{\mathcal{Y}}$ are the total number of actions for the two players. We also propose two Follow the Regularize leader (FTRL) algorithms for this setting: Balanced-FTRL which matches this lower bound, but requires the knowledge of the information set structure beforehand to define the regularization; and Adaptive-FTRL which needs $\mathcal{O}(H^2(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ plays without this requirement by progressively adapting the regularization to the observations.

Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://github.com/cvg/DeepLSD.

We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their own future latent representations. Despite its recent empirical success, such algorithms have an apparent defect: trivial representations (such as constants) minimize the prediction error, yet it is obviously undesirable to converge to such solutions. Our central insight is that careful designs of the optimization dynamics are critical to learning meaningful representations. We identify that a faster paced optimization of the predictor and semi-gradient updates on the representation, are crucial to preventing the representation collapse. Then in an idealized setup, we show self-predictive learning dynamics carries out spectral decomposition on the state transition matrix, effectively capturing information of the transition dynamics. Building on the theoretical insights, we propose bidirectional self-predictive learning, a novel self-predictive algorithm that learns two representations simultaneously. We examine the robustness of our theoretical insights with a number of small-scale experiments and showcase the promise of the novel representation learning algorithm with large-scale experiments.

The increasing complexity of gameplay mechanisms in modern video games is leading to the emergence of a wider range of ways to play games. The variety of possible play-styles needs to be anticipated by designers, through automated tests. Reinforcement Learning is a promising answer to the need of automating video game testing. To that effect one needs to train an agent to play the game, while ensuring this agent will generate the same play-styles as the players in order to give meaningful feedback to the designers. We present CARMI: a Configurable Agent with Relative Metrics as Input. An agent able to emulate the players play-styles, even on previously unseen levels. Unlike current methods it does not rely on having full trajectories, but only summary data. Moreover it only requires little human data, thus compatible with the constraints of modern video game production. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.