Summary of Node Identifiers: Compact, Discrete Representations For Efficient Graph Learning, by Yuankai Luo et al.
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learningby Yuankai Luo, Hongkang Li, Qijiong Liu,…
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learningby Yuankai Luo, Hongkang Li, Qijiong Liu,…
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizerby Zhihan Liu,…
Augmented Risk Prediction for the Onset of Alzheimer’s Disease from Electronic Health Records with Large…
MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Timeby Jikun Kang, Xin Zhe Li,…
Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series…
Unsupervised Meta-Learning via In-Context Learningby Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren, Marlena NowaczykFirst submitted to…
ChatGPT Code Detection: Techniques for Uncovering the Source of Codeby Marc Oedingen, Raphael C. Engelhardt,…
Rethinking Independent Cross-Entropy Loss For Graph-Structured Databy Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu,…
Cross-Validated Off-Policy Evaluationby Matej Cief, Branislav Kveton, Michal KompanFirst submitted to arxiv on: 24 May…
Learning from True-False Labels via Multi-modal Prompt Retrievingby Zhongnian Li, Jinghao Xu, Peng Ying, Meng…