我们引入了三种算法，将模拟重力数据倒入3D地下岩石/流属性。第一种算法是一种基于数据驱动的，基于深度学习的方法，第二个算法将深度学习方法与物理建模混合到单个工作流程中，第三个考虑了表面重力监测的时间依赖性。这些提出的算法的目标应用是地下CO $ _2 $李子作为监视CO $ _2 $固存部部署的补充工具的预测。每种提出的算法的表现都优于传统的反转方法，并在几乎实时实时产生高分辨率的3D地下重建。我们提出的方法以$ \ mu $ gals的形式获得了预测的羽状几何形状和接近完美数据失误的骰子得分。这些结果表明，将4D表面重力监测与深度学习技术相结合代表了一种低成本，快速和非侵入性的方法，用于监测CO $ _2 $存储站点。

具有多级连接的深度神经网络，以复杂的方式进程输入数据来了解信息。网络学习效率不仅取决于复杂的神经网络架构，还取决于输入训练图像。具有用于头骨剥离或肿瘤的深神经网络的Medical图像分段。来自磁共振图像的分割使得能够学习图像的全局和局部特征。虽然收集在受控环境中的医学图像，但可能存在导致输入集中固有偏差的伪影或基于设备的方差。在本研究中，我们调查了具有神经网络分割精度的MR图像的图像质量指标的相关性。我们使用了3D DenSenet架构，并让网络在相同的输入上培训，但应用不同的方法来基于IQM值选择训练数据集。基于随机训练的模型之间的分割精度的差异基于IQM的训练输入揭示了图像质量指标对分割精度的作用。通过运行图像质量指标来选择培训输入，进一步调整网络的学习效率和分割精度。

医学成像深度学习模型通常是大而复杂的，需要专门的硬件来训练和评估这些模型。为了解决此类问题，我们提出了PocketNet范式，以减少深度学习模型的规模，通过促进卷积神经网络中的渠道数量的增长。我们证明，对于一系列的分割和分类任务，PocketNet架构产生的结果与常规神经网络相当，同时将参数数量减少多个数量级，最多使用90％的GPU记忆，并加快训练时间的加快。高达40％，从而允许在资源约束设置中培训和部署此类模型。

We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discovering and linking new (so-called open-world) entities to the knowledge graph. We argue that - though neural approaches to text mining have yielded impressive results in the past years - current benchmarks do not reflect the typical challenges encountered in the industrial wild properly. Therefore, our first contribution is an open benchmark coined IRT2 (inductive reasoning with text) that (1) covers knowledge graphs of varying sizes (including very small ones), (2) comes with incidental, low-quality text mentions, and (3) includes not only triple completion but also ranking, which is relevant for supporting experts with discovery tasks. We investigate two neural models for inductive link prediction, one based on end-to-end learning and one that learns from the knowledge graph and text data in separate steps. These models compete with a strong bag-of-words baseline. The results show a significant advance in performance for the neural approaches as soon as the available graph data decreases for linking. For ranking, the results are promising, and the neural approaches outperform the sparse retriever by a wide margin.

Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.

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.

Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular explanations methods have been found to be fragile to minor perturbations of input features or model parameters. Relying on constraint relaxation techniques from non-convex optimization, we develop a method that upper-bounds the largest change an adversary can make to a gradient-based explanation via bounded manipulation of either the input features or model parameters. By propagating a compact input or parameter set as symbolic intervals through the forwards and backwards computations of the neural network we can formally certify the robustness of gradient-based explanations. Our bounds are differentiable, hence we can incorporate provable explanation robustness into neural network training. Empirically, our method surpasses the robustness provided by previous heuristic approaches. We find that our training method is the only method able to learn neural networks with certificates of explanation robustness across all six datasets tested.

Training a Neural Radiance Field (NeRF) without pre-computed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy.

Predicting the political polarity of news headlines is a challenging task that becomes even more challenging in a multilingual setting with low-resource languages. To deal with this, we propose to utilise the Inferential Commonsense Knowledge via a Translate-Retrieve-Translate strategy to introduce a learning framework. To begin with, we use the method of translation and retrieval to acquire the inferential knowledge in the target language. We then employ an attention mechanism to emphasise important inferences. We finally integrate the attended inferences into a multilingual pre-trained language model for the task of bias prediction. To evaluate the effectiveness of our framework, we present a dataset of over 62.6K multilingual news headlines in five European languages annotated with their respective political polarities. We evaluate several state-of-the-art multilingual pre-trained language models since their performance tends to vary across languages (low/high resource). Evaluation results demonstrate that our proposed framework is effective regardless of the models employed. Overall, the best performing model trained with only headlines show 0.90 accuracy and F1, and 0.83 jaccard score. With attended knowledge in our framework, the same model show an increase in 2.2% accuracy and F1, and 3.6% jaccard score. Extending our experiments to individual languages reveals that the models we analyze for Slovenian perform significantly worse than other languages in our dataset. To investigate this, we assess the effect of translation quality on prediction performance. It indicates that the disparity in performance is most likely due to poor translation quality. We release our dataset and scripts at: https://github.com/Swati17293/KG-Multi-Bias for future research. Our framework has the potential to benefit journalists, social scientists, news producers, and consumers.

Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining \textit{robust} feature attributions. However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods. In this paper, we provide new insights by taking such normalization into account. First, we show that for every non-negative homogeneous neural network, a naive $\ell_2$-robust criterion for gradients is \textit{not} normalization invariant, which means that two functions with the same normalized gradient can have different values. Second, we formulate a normalization invariant cosine distance-based criterion and derive its upper bound, which gives insight for why simply minimizing the Hessian norm at the input, as has been done in previous work, is not sufficient for attaining robust feature attribution. Finally, we propose to combine both $\ell_2$ and cosine distance-based criteria as regularization terms to leverage the advantages of both in aligning the local gradient. As a result, we experimentally show that models trained with our method produce much more robust interpretations on CIFAR-10 and ImageNet-100 without significantly hurting the accuracy, compared to the recent baselines. To the best of our knowledge, this is the first work to verify the robustness of interpretation on a larger-scale dataset beyond CIFAR-10, thanks to the computational efficiency of our method.