最近，使用自动编码器（由使用神经网络建模的编码器，渠道和解码器组成）的通信系统的端到端学习问题最近被证明是一种有希望的方法。实际采用这种学习方法面临的挑战是，在变化的渠道条件（例如无线链接）下，它需要经常对自动编码器进行重新训练，以保持低解码错误率。由于重新培训既耗时又需要大量样本，因此当通道分布迅速变化时，它变得不切实际。我们建议使用不更改编码器和解码器网络的快速和样本（几射击）域的适应方法来解决此问题。不同于常规的训练时间无监督或半监督域的适应性，在这里，我们有一个训练有素的自动编码器，来自源分布，我们希望（在测试时间）使用仅使用一个小标记的数据集和无标记的数据来适应（测试时间）到目标分布。我们的方法着重于基于高斯混合物网络的通道模型，并根据类和组件条件仿射变换制定其适应性。学习的仿射转换用于设计解码器的最佳输入转换以补偿分布变化，并有效地呈现在接近源分布的解码器输入中。在实际MMWAVE FPGA设置以及无线设置共有的许多模拟分布变化上，使用非常少量的目标域样本来证明我们方法在适应时的有效性。

Models of sensory processing and learning in the cortex need to efficiently assign credit to synapses in all areas. In deep learning, a known solution is error backpropagation, which however requires biologically implausible weight transport from feed-forward to feedback paths. We introduce Phaseless Alignment Learning (PAL), a bio-plausible method to learn efficient feedback weights in layered cortical hierarchies. This is achieved by exploiting the noise naturally found in biophysical systems as an additional carrier of information. In our dynamical system, all weights are learned simultaneously with always-on plasticity and using only information locally available to the synapses. Our method is completely phase-free (no forward and backward passes or phased learning) and allows for efficient error propagation across multi-layer cortical hierarchies, while maintaining biologically plausible signal transport and learning. Our method is applicable to a wide class of models and improves on previously known biologically plausible ways of credit assignment: compared to random synaptic feedback, it can solve complex tasks with less neurons and learn more useful latent representations. We demonstrate this on various classification tasks using a cortical microcircuit model with prospective coding.

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time. This has allowed us to compare the performance of many memorability prediction techniques on the same data and in a reproducible way and to refine and improve on those techniques. The resources created to compute media memorability are now being used by researchers well beyond the actual evaluation campaign. In this paper we present a summary of the task, including the collective lessons we have learned for the research community.

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging given the potentially large number of individual and environmental variables involved. This paper shows how to address this problem by using fire as the source of external, bursting and wide fluctuations. Fire propagates on a spatial landscape where a group of agents harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential A.I. management of complex ecosystems are discussed.

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, a smaller, therefore more interpretable Decision Tree can be trained, which, in addition, enhances the stability and robustness of the baseline Decision Tree. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity compared to the baseline Decision Tree.

This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hyponyms with relatively high precision, confirming the manually assessed quality of the resource. The T5 model opens the taxonomy to any new technological terms for which a hypernym can be generated, thus making the resource updateable with new terms, an essential feature for the constantly evolving field of technological terminology.

In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.

In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimization for each individual image or are restricted to single attribute editing. Extending these methods to multi-attribute face image editing scenarios will introduce undesired excessive attribute change, e.g., text-relevant attributes are overly manipulated and text-irrelevant attributes are also changed. In order to address these challenges and achieve natural editing over multiple face attributes, we propose a new decoupling training scheme where we use group sampling to get text segments from same attribute categories, instead of whole complex sentences. Further, to preserve other existing face attributes, we encourage the model to edit the latent code of each attribute separately via an entropy constraint. During the inference phase, our model is able to edit new face images without any test-time optimization, even from complex textual prompts. We show extensive experiments and analysis to demonstrate the efficacy of our method, which generates natural manipulated faces with minimal text-irrelevant attribute editing. Code and pre-trained model will be released.

生成模型生成的合成数据可以增强医学成像中渴望数据深度学习模型的性能和能力。但是，（1）（合成）数据集的可用性有限，并且（2）生成模型训练很复杂，这阻碍了它们在研究和临床应用中的采用。为了减少此入口障碍，我们提出了Medigan，Medigan是一站式商店，用于验证的生成型号，该型号是开源框架 - 不合骨python图书馆。 Medigan允许研究人员和开发人员仅在几行代码中创建，增加和域名。在基于收集的最终用户需求的设计决策的指导下，我们基于生成模型的模块化组件（i）执行，（ii）可视化，（iii）搜索和排名以及（iv）贡献。图书馆的可伸缩性和设计是通过其越来越多的综合且易于使用的验证生成模型来证明的，该模型由21种模型组成，利用9种不同的生成对抗网络体系结构在4个域中在11个数据集中训练，即乳腺摄影，内窥镜检查，X射线和X射线和X射线镜头，X射线和X型。 MRI。此外，在这项工作中分析了Medigan的3个应用，其中包括（a）启用社区范围内的限制数据共享，（b）研究生成模型评估指标以及（c）改进临床下游任务。在（b）中，扩展了公共医学图像综合评估和报告标准，我们根据图像归一化和特定于放射学特征提取了Fr \'Echet Inception距离变异性。

在（特殊的）平滑样条问题中，一个人考虑了二次数据保真惩罚和拉普拉斯正则化的变异问题。可以通过用聚拉普拉斯的正规机构代替拉普拉斯的常规机构来获得较高的规律性。该方法很容易适应图，在这里，我们考虑在完全监督的，非参数，噪声损坏的回归问题中图形多拉普拉斯正则化。特别是，给定一个数据集$ \ {x_i \} _ {i = 1}^n $和一组嘈杂的标签$ \ {y_i \} _ {i = 1}^n \ subset \ subset \ mathbb {r}令$ u_n：\ {x_i \} _ {i = 1}^n \ to \ mathbb {r} $是由数据保真项组成的能量的最小化器，由数据保真术语和适当缩放的图形poly-laplacian项组成。当$ y_i = g（x_i）+\ xi_i $，对于IID噪声$ \ xi_i $，并使用几何随机图，我们在大型中识别（高概率）$ u_n $ to $ g $的收敛速率数据限制$ n \ to \ infty $。此外，我们的速率（到对数）与通常的平滑样条模型中已知的收敛速率相吻合。