Integration of cognitive tasks into artificial general intelligence test for large models.

During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence.

Cross-dependent graph neural networks for molecular property prediction.

Motivation: The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through graph neural networks (GNNs). Both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model ought to exploit both node (atom) and edge (bond) information simultaneously.

Recognizing Predictive Substructures With Subgraph Information Bottleneck.

The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack of interpretation for prediction results, impede further development of GCN. One solution is to recognize a predictive yet compressed subgraph to get rid of the noise and redundancy and obtain the interpretable part of the graph.

Parallel Coordinate Descent Newton Method for Efficient L -Regularized Loss Minimization.

The recent years have witnessed advances in parallel algorithms for large-scale optimization problems. Notwithstanding the demonstrated success, existing algorithms that parallelize over features are usually limited by divergence issues under high parallelism or require data preprocessing to alleviate these problems. In this paper, we propose a Parallel Coordinate Descent algorithm using approximate Newton steps (PCDN) that is guaranteed to converge globally without data preprocessing.