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Found 8 papers, 4 papers with code
Adversarial Attack against Cross-lingual Knowledge Graph Alignment
Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks.
Successfully Applying Lottery Ticket Hypothesis to Diffusion Model
Therefore, we propose to find the winning ticket with varying sparsity along different layers in the model.
Towards Understanding Sycophancy in Language Models
Overall, our results indicate that sycophancy is a general behavior of state-of-the-art AI assistants, likely driven in part by human preference judgments favoring sycophantic responses.
Rethinking Graph Lottery Tickets: Graph Sparsity Matters
Therefore, we propose two techniques to improve GNN performance when the graph sparsity is high.
Discovering Language Model Behaviors with Model-Written Evaluations
We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse.
Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams
Specifically, we propose a weighted sampling algorithm called WSD for estimating the subgraph count in a fully dynamic graph stream, which samples the edges based on their weights that indicate their importance and reflect their properties.
Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint
These methods, however, assume that incomplete feature observations only happen on a small subset of samples, and thus cannot solve problems where the vast majority of samples have missing feature observations.
Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy
In contrast, we propose to represent surface topological structure by a contour tree skeleton, which is a polytree capturing the evolution of surface contours at different elevation levels.
