Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.

Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.

While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset. Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. The core of our approach is a measurement Temporal Output Discrepancy (TOD) that estimates the sample loss by evaluating the discrepancy of outputs given by models at different optimization steps. Our theoretical investigation shows that TOD lower-bounds the accumulated sample loss thus it can be used to select informative unlabeled samples. On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion for active learning. Due to the simplicity of TOD, our methods are efficient, flexible, and task-agnostic. Extensive experimental results demonstrate that our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks. In addition, we show that TOD can be utilized to select the best model of potentially the highest testing accuracy from a pool of candidate models.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) that maximizes the long-term expected sum rate under the users' packet dropping ratio constraints. In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named federated Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local time-frequency configurations through RL algorithms and achieve global training via exchanging local RL models with their neighbors under a decentralized federated learning framework. Specifically, to deal with the large-scale discrete action space of each BS, we adopt a DDPG-based algorithm to generate actions in a continuous space, and then utilize Wolpertinger policy to reduce the mapping errors from continuous action space back to discrete action space. Simulation results demonstrate the superiority of our proposed algorithm to benchmark algorithms with respect to system sum rate.

命名实体识别（NER）任务旨在识别属于人，位置，组织等预定语义类型的文本中的实体。平面实体的最新解决方案NER通常因捕获捕获基础文本中的细粒语义信息。现有的基于跨度的方法克服了这一限制，但是计算时间仍然是一个问题。在这项工作中，我们提出了一个基于跨度的新型NER框架，即全球指针（GP），该框架通过乘法注意机制来利用相对位置。最终目标是实现一个全球观点，以考虑开始和最终位置以预测实体。为此，我们设计了两个模块来识别给定实体的头部和尾部，以使训练和推理过程之间的不一致。此外，我们引入了一种新型的分类损失函数，以解决不平衡标签问题。在参数方面，我们引入了一种简单但有效的近似方法来减少训练参数。我们在各种基准数据集上广泛评估GP。我们的广泛实验表明，GP可以胜过现有的解决方案。此外，实验结果表明，与软马克斯和熵替代方案相比，引入的损失函数的功效。

最近，先驱研究工作提出了大量的声学特征（原木功率谱图，线性频率卷轴系数，恒定的q cepstral系数等），以进行音频深层检测，获得良好的性能，并表明不同的子带对音频有不同的贡献DeepFake检测。但是，这缺乏对子带中特定信息的解释，这些功能也丢失了诸如阶段之类的信息。受合成语音机制的启发，基本频率（F0）信息用于提高综合语音的质量，而合成语音的F0仍然太平均，这与真实语音的F0差异很大。可以预期，F0可以用作重要信息来区分真正的语言和虚假语音，而由于F0的分布不规则，因此不能直接使用此信息。相反，选择了大多数F0的频带作为输入特征。同时，为了充分利用相位和全频段信息，我们还建议使用真实和虚构的频谱图作为互补输入功能，并分别对Discoint子带进行建模。最后，融合了F0的结果，真实和假想的频谱图。 ASVSPOOF 2019 LA数据集的实验结果表明，我们所提出的系统对于音频DeepFake检测任务非常有效，达到等效错误率（EER）为0.43％，几乎超过了所有系统。

GPT系列和BERT等大型序列模型（SM）在视觉，语言以及最近的强化学习任务上表现出了出色的性能和概括功能。一个自然的后续问题是如何将多代理决策抽象成SM问题，并受益于SMS的繁荣发展。在本文中，我们介绍了一种名为多代理变压器（MAT）的新型架构，该结构有效地将合作的多代理增强学习（MARL）施加到SM问题中，其中任务是将代理的观察顺序映射到代理的最佳动作序列中。我们的目标是在Marl和SMS之间建造桥梁，以便为MARL释放现代序列模型的建模能力。我们垫子的核心是一个编码器架构，它利用多代理优势分解定理将联合策略搜索问题转换为顺序决策过程。这仅适用于多代理问题的线性时间复杂性，最重要的是，具有单调性能改进保证。与以前的艺术（例如Decorment Transformer Fit仅预先收集的离线数据）不同，MAT通过在线试验和环境中的错误进行培训。为了验证MAT，我们对StarcraftII，多代理Mujoco，灵巧的手操纵和Google Research Football Benchmarks进行了广泛的实验。结果表明，与Mappo和Happo在内的强大基线相比，MAT可实现卓越的性能和数据效率。此外，我们证明MAT是一位出色的少数人，无论代理人的数量变化如何，MAT都是看不见的任务。请参阅我们的项目页面，网址为https://sites.google.com/view/multi-agent-transformer。

点云降级旨在从噪音和异常值损坏的原始观察结果中恢复清洁点云，同时保留细粒细节。我们提出了一种新型的基于深度学习的DeNoising模型，该模型结合了正常的流量和噪声解散技术，以实现高降解精度。与提取点云特征以进行点校正的现有作品不同，我们从分布学习和特征分离的角度制定了denoising过程。通过将嘈杂的点云视为清洁点和噪声的联合分布，可以从将噪声对应物从潜在点表示中解​​散出来，而欧几里得和潜在空间之间的映射是通过标准化流量来建模的。我们评估了具有各种噪声设置的合成3D模型和现实世界数据集的方法。定性和定量结果表明，我们的方法表现优于先前的最先进的基于深度学习的方法。

在空中杂种大规模多输入多输出（MIMO）和正交频施加多路复用（OFDM）系统中，如何设计具有有限的飞行员和反馈开销的光谱效率宽带多用户混合波束，这是具有挑战性的。为此，通过将关键传输模块建模为端到端（E2E）神经网络，本文提出了一个数据驱动的深度学习（DL）基于时间划分双工（TDD）的基于数据驱动的深度学习（DL）的统一混合边际框架和具有隐式通道状态信息（CSI）的频分隔双链（FDD）系统。对于TDD系统，提出的基于DL的方法共同对上行链路飞行员组合和下行链路混合光束模块作为E2E神经网络。在FDD系统中，我们将下行链路飞行员传输，上行链路CSI反馈和下行链路混合光束形成模块作为E2E神经网络建模。与分别处理不同模块的常规方法不同，提出的解决方案同时以总和速率作为优化对象优化了所有模块。因此，通过感知空对地面大规模MIMO-OFDM通道样本的固有属性，基于DL的E2E神经网络可以建立从通道到波束形式的映射函数，以便可以避免使用显式通道重建，以减少飞行员和反馈开销。此外，实用的低分辨率相变（PSS）引入了量化约束，从而导致训练神经网络时棘手的梯度反向传播。为了减轻阶段量化误差引起的性能损失，我们采用转移学习策略，以基于假定理想的无限分辨率PSS的预训练网络来进一步调整E2E神经网络。数值结果表明，我们的基于DL的方案比最先进的方案具有相当大的优势。