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Found 6 papers, 2 papers with code
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values.
T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration
This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors.
GIR Framework: Learning Graph Positional Embeddings with Anchor Indication and Path Encoding
In this work, we propose a novel framework which follows the anchor-based idea and aims at conveying distance information implicitly along the MPNN message passing steps for encoding position information, node attributes, and graph structure in a more flexible way.
Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding
We show that GIRs get outperformed results in position-aware scenarios, and performances on typical GNNs could be improved by fusing GIR embeddings.
Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training
To further improve scalable models on semi-supervised learning tasks, we propose Self-Label-Enhance (SLE) framework combining self-training approach and label propagation in depth.
Ranked #4 on
Node Property Prediction
on ogbn-papers100M
Adaptive Graph Diffusion Networks
Until the date of submission (Aug 26, 2022), AGDNs achieve top-1 performance on the ogbn-arxiv, ogbn-proteins and ogbl-ddi datasets and top-3 performance on the ogbl-citation2 dataset.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
