arXiv Machine Learning

RADE: Random Add-Drop Edge as a Regularizer

arXiv:2606. 00757v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) suffer from overfitting and over-squashing of long-range information.

arXiv AI

Jul 7

Hyperparameter Transfer in Graph Neural Networks

arXiv:2607. 05017v1 Announce Type: cross Abstract: The performance of deep learning models crucially depends on the settings of hyperparameters like learning rate, initialization scale, and weight decay.

By Gage DeZoort, Boris Hanin

arXiv Machine Learning

Jul 27

Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement

arXiv:2607. 21885v1 Announce Type: new Abstract: Coarsening-based training for graph neural networks (GNNs), i.

By Guoming Li, Jian Yang, Xukun Wang, Zixiao Wang, Shangsong Liang, Yifan Chen

benchmarks

arXiv Machine Learning

Jul 8

Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning

arXiv:2607. 06151v1 Announce Type: new Abstract: Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting and reduced performance on unseen data.

By Yao Fu, Chunxia Zhang, Junmin Liu, Yihang Jin, Haishan Ye, Yuanao Yang

benchmarks

arXiv Machine Learning

Jul 17

Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance

arXiv:2607. 14466v1 Announce Type: new Abstract: Noise injection is a well-known technique in stochastic optimization.

By Matt L. Wiemann, Peter Melchior, Andrew K. Saydjari

safety

arXiv Machine Learning

Aug 10

SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction

arXiv:2608. 06441v1 Announce Type: new Abstract: Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges.

By Guofan Yu, Sitian Chen, Zhenheng Tang, Xiaowen Chu, Amelie Chi Zhou

rag benchmarks safety data-engineering

arXiv Machine Learning

Jun 15

Generalizing GNNs with Tokenized Mixture of Experts

arXiv:2602. 09258v2 Announce Type: replace Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations.

By Xiaoguang Guo, Zehong Wang, Jiazheng Li, Shawn Spitzel, Qi Yang, Kaize Ding, Jundong Li, Chuxu Zhang

efficiency benchmarks

arXiv Machine Learning

Jul 14

Random Label Prediction Heads for Studying Memorization in Deep Neural Networks

arXiv:2607. 11541v1 Announce Type: new Abstract: We introduce a straightforward yet effective method to empirically study memorization in deep neural networks for classification tasks.

By Marlon Becker, Jonas Konrad, Luis Garcia Rodriguez, Benjamin Risse

arXiv Machine Learning

1d ago

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.

By Yuhang Song, Naima Abrar Shami, Romaric Duvignau, Vasiliki Kalavri

arXiv Machine Learning

Jul 21

Node4All: Learning Node Representation Beyond Datasets

arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.

By Dooho Lee, Jaemin Yoo

llms benchmarks

arXiv Machine Learning

Jun 2

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs

arXiv:2606. 01873v1 Announce Type: new Abstract: LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embedding space via CLIP-style contrastive learning.

By Yuhan Wang, Yibo Ding, Yutong Ye, Mufan Zhao, Wenbo Zhang, Ruijie Wang, Jianxin Li

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

22h ago

Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks

arXiv:2509. 06777v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require long-range interactions.

By Kushal Bose, Swagatam Das

benchmarks safety

arXiv Machine Learning

Jun 5

Towards Label-Noise Resistant Learning via Optimal Brain Damage Masking

arXiv:2508. 09697v3 Announce Type: replace Abstract: Noisy labels are inevitable in real-world scenarios.

By Xinlei Zhang, Fan Liu, Chuanyi Zhang, Fan Cheng, Qian Li, Yuhui Zheng

efficiency benchmarks

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