It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainly resolve this issue by retraining the entire model to impose the uncertainty quantification capability so that the learned model can achieve desired performance in accuracy and uncertainty prediction simultaneously. However, training the model from scratch is computationally expensive and may not be feasible in many situations. In this work, we consider a more practical post-hoc uncertainty learning setting, where a well-trained base model is given, and we focus on the uncertainty quantification task at the second stage of training. We propose a novel Bayesian meta-model to augment pre-trained models with better uncertainty quantification abilities, which is effective and computationally efficient. Our proposed method requires no additional training data and is flexible enough to quantify different uncertainties and easily adapt to different application settings, including out-of-domain data detection, misclassification detection, and trustworthy transfer learning. We demonstrate our proposed meta-model approach's flexibility and superior empirical performance on these applications over multiple representative image classification benchmarks.

我们通过查看在弥漫表面上铸造的对象的阴影来研究个体的生物特征识别信息的问题。我们表明，通过最大似然分析，在代表性的情况下，阴影中的生物特征信息泄漏可以足够用于可靠的身份推断。然后，我们开发了一种基于学习的方法，该方法在实际设置中证明了这种现象，从而利用阴影中的微妙提示是泄漏的来源，而无需任何标记的真实数据。特别是，我们的方法依赖于构建由从每个身份的单个照片获得的3D面模型组成的合成场景。我们以完全无监督的方式将我们从合成数据中学到的知识转移到真实数据中。我们的模型能够很好地概括到真实的域，并且在场景中的几种变体都有坚固的范围。我们报告在具有未知几何形状和遮挡对象的场景中发生的身份分类任务中的高分类精度。

我们研究了数据驱动的深度学习方法的潜力，即从观察它们的混合物中分离两个通信信号。特别是，我们假设一个信号之一的生成过程（称为感兴趣的信号（SOI）），并且对第二个信号的生成过程不了解，称为干扰。单通道源分离问题的这种形式也称为干扰拒绝。我们表明，捕获高分辨率的时间结构（非平稳性），可以准确地同步与SOI和干扰，从而带来了可观的性能增长。有了这个关键的见解，我们提出了一种域信息神经网络（NN）设计，该设计能够改善“现成” NNS和经典检测和干扰拒绝方法，如我们的模拟中所示。我们的发现突出了特定于交流领域知识的关键作用在开发数据驱动的方法方面发挥了作用，这些方法具有前所未有的收益的希望。

我们研究了单通道源分离（SCSS）的问题，并专注于环化信号，这些信号特别适用于各种应用领域。与经典的SCSS方法不同，我们考虑了一个仅可用源的示例而不是模型的设置，从而激发了数据驱动的方法。对于具有基本环化高斯成分的源模型，我们为任何基于模型或数据驱动的分离方法建立了可达到的均方误差（MSE）的下限。我们的分析进一步揭示了最佳分离和相关实施挑战的操作。作为一种计算吸引力的替代方案，我们建议使用U-NET体系结构进行深度学习方法，该方法与最低MSE估计器具有竞争力。我们在模拟中证明，有了合适的域信息架构选择，我们的U-NET方法可以通过大幅减少的计算负担来达到最佳性能。

直接定位（DLOC）方法，该方法使用观察到的数据将源定位在一步过程中的未知位置，通常优于其间接的两步对应物（例如，使用到达的时间差异）。但是，水下声学DLOC方法需要对环境的先验知识，并且计算昂贵，因此很慢。我们建议，据我们所知，这是第一个数据驱动的DLOC方法。受经典和现代最佳模型的DLOC解决方案的启发，并利用了卷积神经网络（CNN）的功能，我们设计了一个基于CNN的整体解决方案。我们的方法包括专门量身定制的输入结构，体系结构，损失功能和渐进培训程序，在更广泛的机器学习背景下具有独立的兴趣。我们证明我们的方法优于有吸引力的替代方案，并且渐近地与基于Oracle的最佳模型解决方案的性能匹配。

我们通过专注于两个流行的转移学习方法，$ \ Alpha $ -weighted-ERM和两级eRM，提供了一种基于GIBBS的转移学习算法的泛化能力的信息 - 理论分析。我们的关键结果是使用输出假设和给定源样本的输出假设和目标训练样本之间的条件对称的KL信息进行精确表征泛化行为。我们的结果也可以应用于在这两个上述GIBBS算法上提供新的无分布泛化误差上限。我们的方法是多才多艺的，因为它还表征了渐近误差和渐近制度中这两个GIBBS算法的过度风险，它们分别收敛到$ \ alpha $ -winution-eRM和两级eRM。基于我们的理论结果，我们表明，转移学习的好处可以被视为偏差折衷，源分布引起的偏差和缺乏目标样本引起的差异。我们认为这一观点可以指导实践中转移学习算法的选择。

如果对准确的预测的置信度不足，则选择性回归允许弃权。通常，通过允许拒绝选项，人们期望回归模型的性能会以减少覆盖范围的成本（即预测较少的样本）的成本提高。但是，正如我们所显示的，在某些情况下，少数子组的性能可以减少，同时我们减少覆盖范围，因此选择性回归可以放大不同敏感亚组之间的差异。在这些差异的推动下，我们提出了新的公平标准，用于选择性回归，要求每个子组的性能在覆盖范围内降低。我们证明，如果特征表示满足充分性标准或为均值和方差进行校准，则与所提出的公平标准相比。此外，我们介绍了两种方法，以减轻子组之间的性能差异：（a）通过在高斯假设下正规化有条件相互信息的上限，以及（b）通过对条件均值和条件方差预测的对比度损失正规。这些方法的有效性已在合成和现实世界数据集上证明。

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.

Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets, and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as open source at https://github.com/gregory-kyro/HAC-Net/.

In recent years several learning approaches to point goal navigation in previously unseen environments have been proposed. They vary in the representations of the environments, problem decomposition, and experimental evaluation. In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem. We adapt the (POMDP) sub-goal framework proposed by [1] and modify the component that estimates frontier properties by using partial semantic maps of indoor scenes built from images' semantic segmentation. In addition to the well-known completeness of the model-based approach, we demonstrate that it is robust and efficient in that it leverages informative, learned properties of the frontiers compared to an optimistic frontier-based planner. We also demonstrate its data efficiency compared to the end-to-end deep reinforcement learning approaches. We compare our results against an optimistic planner, ANS and DD-PPO on Matterport3D dataset using the Habitat Simulator. We show comparable, though slightly worse performance than the SOTA DD-PPO approach, yet with far fewer data.