课堂学习学习需要可塑性和稳定性，以便在保留过去的知识的同时从新数据中学习。由于灾难性的遗忘，当没有内存缓冲区可用时，在这两个属性之间找到妥协尤其具有挑战性。主流方法需要存储两个深层模型，因为它们使用微调与以前的增量状态的知识蒸馏一起整合了新类。我们提出了一种具有相似数量参数但分布不同的方法，以便在可塑性和稳定性之间找到更好的平衡。遵循已经通过基于转移的增量方法部署的方法，我们在初始状态后冻结了功能提取器。最古老的增量状态的类对这种冷冻提取器进行训练，以确保稳定性。使用部分微调模型预测最近的类别以引入可塑性。我们提出的可塑性层可以纳入任何用于无内存增量学习的基于转移的方法，并将其应用于两种此类方法。评估是通过三个大型数据集进行的。结果表明，与现有方法相比，所有测试的配置中均获得了性能提高。

Explainable AI transforms opaque decision strategies of ML models into explanations that are interpretable by the user, for example, identifying the contribution of each input feature to the prediction at hand. Such explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by finding relevant subspaces in activation space that can be mapped to more abstract human-understandable concepts and enable a joint attribution on concepts and input features. To automatically extract the desired representation, we propose new subspace analysis formulations that extend the principle of PCA and subspace analysis to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), optimize relevance of projected activations rather than the more traditional variance or kurtosis. This enables a much stronger focus on subspaces that are truly relevant for the prediction and the explanation, in particular, ignoring activations or concepts to which the prediction model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.

In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this task. In addition, we present how such models may be pretrained in a multimodal scenario to improve emotion recognition performances. We evaluate the benefits of using multimodal inputs and pre-training with our approach on a state-ofthe-art dataset.

A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute. Verifyber implementation and trained models are available at https://github.com/FBK-NILab/verifyber.

White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIber gEneration and bundle Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate WM bundles. Our framework allows the transition from one anatomical bundle definition to another with marginal calibrating time. This pipeline is built upon FINTA, CINTA, and GESTA methods that demonstrated how autoencoders can be used successfully for streamline filtering, bundling, and streamline generation in tractography. Our proposed method improves bundling coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase each bundle's spatial coverage while remaining anatomically meaningful. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundling framework

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

可靠的概括是安全ML和AI的核心。但是，了解神经网络何时以及如何推广仍然是该领域最重要的未解决问题之一。在这项工作中，我们进行了一项广泛的实证研究（2200个模型，16个任务），以研究计算理论中的见解是否可以预测实践中神经网络概括的局限性。我们证明，根据Chomsky层次结构进行分组任务使我们能够预测某些架构是否能够推广到分布外输入。这包括负面结果，即使大量数据和训练时间也不会导致任何非平凡的概括，尽管模型具有足够的能力完美地适合培训数据。我们的结果表明，对于我们的任务子集，RNN和变形金刚无法概括非规范的任务，LSTMS可以解决常规和反语言任务，并且只有通过结构化内存（例如堆栈或存储器磁带）可以增强的网络可以成功地概括了无上下文和上下文敏感的任务。

NLP（例如基于过渡的解析）的贪婪算法容易出现误差传播。克服这个问题的一种方法是允许算法回溯并探索替代解决方案，在新证据与迄今为止探索的解决方案相矛盾的情况下。为了实施这种行为，我们使用增强学习，并在此操作获得更好的奖励的情况下让算法回溯，而不是继续探索当前的解决方案。我们在POS标签和依赖解析上测试了这一想法，并表明回溯是反对错误传播的有效手段。

这项工作提出了一个新的程序，可以在高斯过程（GP）建模的背景下获得预测分布，并放松了一些感兴趣的范围之外的插值约束：预测分布的平均值不一定会在观察到的值时插入观察值的值。感兴趣的外部范围，但仅限于留在外面。这种称为放松的高斯工艺（REGP）插值的方法在感兴趣的范围内提供了更好的预测分布，尤其是在GP模型的平稳性假设不合适的情况下。它可以被视为一种面向目标的方法，并且在贝叶斯优化中变得特别有趣，例如，对于目标函数的最小化，低功能值的良好预测分布很重要。当将预期改进标准和REGP用于依次选择评估点时，从理论上保证了所得优化算法的收敛性（前提）。实验表明，在贝叶斯优化中使用REGP代替固定的GP模型是有益的。

诸如最大熵正则化之类的政策正则化方法被广泛用于增强学习以提高学习政策的鲁棒性。在本文中，我们展示了这种鲁棒性是如何通过对冲的奖励功能扰动而产生的，奖励功能是从想象中的对手设定的限制设置中选择的。使用凸双重性，我们表征了KL和Alpha-Divergence正则化的一组强大的对抗奖励扰动集，其中包括香农和Tsallis熵正则定期为特殊情况。重要的是，可以在此强大集合中给出概括保证。我们提供了有关最坏的奖励扰动的详细讨论，并提供了直观的经验示例，以说明这种稳健性及其与概括的关系。最后，我们讨论我们的分析如何补充并扩展对对抗奖励鲁棒性和路径一致性最佳条件的先前结果。