Graph Regression On Pcqm4Mv2 Lsc

Métriques

Test MAE

Validation MAE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Test MAE	Validation MAE	Paper Title	Repository
EGT	0.0862	0.0857	Global Self-Attention as a Replacement for Graph Convolution	-
GPTrans-L	0.0821	0.0809	Graph Propagation Transformer for Graph Representation Learning	-
GPS	0.0862	0.0852	Recipe for a General, Powerful, Scalable Graph Transformer	-
TIGT	-	0.0826	Topology-Informed Graph Transformer	-
Graphormer + GFSA	-	0.0860	Graph Convolutions Enrich the Self-Attention in Transformers!	-
Graphormer	-	0.0864	Do Transformers Really Perform Bad for Graph Representation?	-
EGT+SSA+Self-ensemble	-	0.0865	The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles	-
GCN	0.1398	0.1379	Semi-Supervised Classification with Graph Convolutional Networks	-
ESA (Edge set attention, no positional encodings)	N/A	0.0235	An end-to-end attention-based approach for learning on graphs	-
MLP-Fingerprint	0.1760	0.1753	OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs	-
GRIT	-	0.0859	Graph Inductive Biases in Transformers without Message Passing	-
Uni-Mol+	0.0705	0.0693	Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+	-
GRPE-Large	0.0876	0.0867	GRPE: Relative Positional Encoding for Graph Transformer	-
TokenGT	0.0919	0.0910	Pure Transformers are Powerful Graph Learners	-
EGT + Triangular Attention	0.0683	0.0671	Global Self-Attention as a Replacement for Graph Convolution	-
TGT-At	0.0683	0.0671	Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers	-
GIN	0.1218	0.1195	How Powerful are Graph Neural Networks?	-
GPTrans-T	0.0842	0.0833	Graph Propagation Transformer for Graph Representation Learning	-
Transformer-M	0.0782	0.0772	One Transformer Can Understand Both 2D & 3D Molecular Data	-
EGT+SSA	-	0.0876	The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles	-

0 of 20 row(s) selected.