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Unifying Generation and Prediction on Graphs with Latent Graph Diffusion Cai Zhou
However, compared with the huge success of generative models in natural language processing [Tou-vron et al., 2023] and computer vision [Rombach et al., 2021], graph generation is faced with many
On the Gini-impurity Preservation For Privacy Random Forests
This work takes one step towards data encryption by incorporating some crucial ingredients of learning algorithm.
BOLD: Boolean Logic Deep Learning
Computational intensiveness of deep learning has motivated low-precision arithmetic designs.
Adaptive Contextual Perception: How to Generalize to New Backgrounds and Ambiguous Objects
Motivated by our analysis results, we propose new data augmentation methods aimed at enhancing model generalization.
PT ADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
The focus of our work is on diagnostic tasks in personalized learning, such as cognitive diagnosis and knowledge tracing. The goal of these tasks is to assess students'
A Experiment on zero-shot classification
The top two rows show easy cases, while the bottom three rows present hard cases, including crowdedness, complex backgrounds, and tiny objects.
Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition Wei He, Kai Han
The development of foundation vision models has pushed the general visual recognition to a high level, but cannot well address the fine-grained recognition in specialized domain such as invasive species classification. Identifying and managing invasive species has strong social and ecological value.