InCM-GCL, we can either takethe textfeaturexT orthe image featurexI asthe content feature, and consider the corresponding text encoderfT or image encoderfI as the content encoder. In this section, we discuss the settings of baseline models for imbalanced node classification over fourgraphs. G1: We convert the rich text content into the bag-of-words feature vectors, and further feed the feature vectors with different imbalance ratios to a two-layer MLP [7] classifier to get the classification results. For AMiner, YelpChi, and GitHub graph datasets, we implement CHI-Square [11]toselect useful feature words. G2: We implement three graph neural network based representation learning models including GCN [5], GAT [9], and GraphSAGE [2] to learn the node embeddings by leveraging both node feature (bag-of-words feature vector) andgraph structure information.

They are able to learn anti-correlated instances, i.e., defaulting to "positive" labels until seeing a negative counter-example, which should not be possible for a correct MIL model.

Sharma et al. (2022) provide Y ang et al. (2022a) integrate Local SGDA with stochastic gradient estimators to eliminate the More recently, Zhang et al. (2023) adopt compressed momentum methods with Local SGD to increase the communication efficiency of the algorithm. For centralized nonconvex minimax problems, Y ang et al. (2022b) show that, even in deterministic settings, GDA-based methods necessitate the timescale separation of the stepsizes for primal and dual updates.

By assessing human annotators as classifiers, we remove the subjective quality of existing evaluation metrics.

KL divergence between the trained posterior distribution obtained by infinitesimal step size gradient descent and a Gaussian prior.

However, when using a CNN to model the ventral stream, the visual spatial extent of the model's input is of key importance to ensure that it is correctly mapped to the data it is trying to We set the high attack strength at 4 times the low value, which brought standard ImageNet trained CNNs to nearly chance performance. Still, even this higher perturbation strength remained practically imperceptible (Fig. B.3 left). The Adversarial Robustness Toolkit [96] was used for computing the attacks. VOneResNet50 improves robustness to white box attacks in a wide range of perturbation strengths. Adding the VOneBlock to ResNet50 consistently improves robustness under all constraints and attack strength--VOneResNet50 V alues are mean and SD (n=3 seeds).White box PGD-L Further attack optimization does not overturn results.