Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts

In reliable decision-making systems based on machine learning, models have to be robust to distributional shifts or provide the uncertainty of their predictions. In node-level problems of graph learning, distributional shifts can be especially complex since the samples are interdependent. To evaluate the performance of graph models, it is important to test them on diverse and meaningful distributional shifts. However, most graph benchmarks considering distributional shifts for node-level problems focus mainly on node features, while structural properties are also essential for graph problems. In this work, we propose a general approach for inducing diverse distributional shifts based on graph structure. We use this approach to create data splits according to several structural node properties: popularity, locality, and density. In our experiments, we thoroughly evaluate the proposed distributional shifts and show that they can be quite challenging for existing graph models. We also reveal that simple models often outperform more sophisticated methods on these challenging shifts. Finally, our experiments provide evidence that there is a trade-off between the quality of learned representations for the base classification task under structural distributional shift and the ability to separate the nodes from different distributions using these representations

Use of Perovskites for Optosensors

The work is devoted to the determination of the place of perovskites among the other materials for optosensors. The features and advantages of this material are determined. The methods for the manufacture of perovskites and the latest technical developments in their application are analyzed. Perovskite and silicon photosensors are compared. A comparative table of perovskite compounds is drawn up. It is determined that perovskites have great potential for their use in low-voltage, inexpensive, high-speed, highly sensitive and ultra-highly integrated optoelectronic devices

Use of Perovskites for Optosensors

The work is devoted to the determination of the place of perovskites among the other materials for optosensors. The features and advantages of this material are determined. The methods for the manufacture of perovskites and the latest technical developments in their application are analyzed. Perovskite and silicon photosensors are compared. A comparative table of perovskite compounds is drawn up. It is determined that perovskites have great potential for their use in low-voltage, inexpensive, high-speed, highly sensitive and ultra-highly integrated optoelectronic devices

ICML 2023 Topological Deep Learning Challenge:Design and Results

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two month duration. This paper describes the design of the challenge and summarizes its main findings.</p

ICML 2023 topological deep learning challenge. Design and results

This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main finding