arXiv Machine Learning
Aug 31

Node-wise Feature Encoding for Neural Performance Prediction

FeatureFormer is a neural performance predictor that adds explicit node-wise encodings of FLOPs, parameter counts, and memory proxies to a gated graph attention architecture. It is designed to improve latency and energy prediction for neural networks on edge devices, addressing the limitation of existing GNN and transformer predictors that largely ignore node-level computational cost. The authors also introduce NNEQ, a large-scale energy consumption dataset, and show through extensive experiments that FeatureFormer achieves state‑of‑the‑art performance across both metrics, including challenging out‑of‑domain settings, while the encoding can broadly enhance existing predictors with negligible overhead.

By Matthew Grenier, William Hammer, Andrew Heuer, Nikhil Krishna, Yi Wang, Ramtin Zand
arXiv Machine Learning
Aug 28

Plain Transformers Can be Powerful Graph Learners

The paper shows that a plain Transformer can serve as an effective graph learner by adding three lightweight modifications: simplified L₂ attention, adaptive RMS normalization, and an MLP-based positional encoding stem. These changes preserve token magnitude and enable the model to achieve high expressivity on graph benchmarks, outperforming more complex graph transformer variants. The results suggest that plain Transformers can act as a unified backbone for multimodal learning across language, vision, and graph domains.

By Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Philip H. S. Torr, Mark Coates
arXiv Machine Learning
Oct 28

Gating and Learnable Temperature for Graph Attention Mechanisms

The paper introduces gated graph attention and a learnable temperature to enhance standard graph attention networks. Gating filters out unreliable feature dimensions, while the temperature parameter adjusts the sharpness of attention coefficient distributions. Experiments on node classification and heterophilic benchmarks show that these modifications improve performance and robustness, with theoretical analysis supporting the observed gains.

By Zhongtian Ma, Hao Wu, Yexin Zhang, Qiaosheng Zhang, Chao Gao