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Found 10 papers, 5 papers with code
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples.
Diffusion-Driven Domain Adaptation for Generating 3D Molecules
These structure variations are encoded with an equivariant encoder and treated as domain supervisors to control denoising.
SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer
Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics.
Practical Differentially Private and Byzantine-resilient Federated Learning
In contrast, we leverage the random noise to construct an aggregation that effectively rejects many existing Byzantine attacks.
SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency
For this purpose, we develop a prompt based on the features of depth differences between the hazy input images and corresponding clear counterparts that can guide dehazing models for better restoration.
1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)
Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe.
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
Towards Private Learning on Decentralized Graphs with Local Differential Privacy
It is typical to collect these local views of social graphs and conduct graph learning tasks.
Interpreting Graph Neural Networks via Unrevealed Causal Learning
This paper proposes a new explanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
Generative Causal Explanations for Graph Neural Networks
Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task.
