1.3k citations · 1.5k across the 47 of their papers we have counts for
12 papers · 1 filter
KumoRFM-2: Scaling Foundation Models for Relational Learning
Valter Hudovernik, Federico López, Vid Kocijan +4
We introduce KumoRFM-2, the next iteration of a pre-trained foundation model for relational data. KumoRFM-2 supports in-context learning as well as fine-tuning and is applicable to…
PyG 2.0: Scalable Learning on Real World Graphs
Matthias Fey, Jinu Sunil, Akihiro Nitta +10
PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0…
Spatial Reasoners for Continuous Variables in Any Domain
Bart Pogodzinski, Christopher Wewer, Bernt Schiele +1
We present Spatial Reasoners, a software framework to perform spatial reasoning over continuous variables with generative denoising models. Denoising generative models have become…
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
Haiyang Wang, Yue Fan, Muhammad Ferjad Naeem +5
Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these…
RelBench: A Benchmark for Deep Learning on Relational Databases
Joshua Robinson, Rishabh Ranjan, Weihua Hu +9
We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse do…
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Jialin Chen, Jan Eric Lenssen, Aosong Feng +5
Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting pe…
Relational Deep Learning: Graph Representation Learning on Relational Databases
Matthias Fey, Weihua Hu, Kexin Huang +6
Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations.…
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Matthias Fey, Jan E. Lenssen, Frank Weichert +1
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical…
Deep Graph Matching Consensus
Matthias Fey, Jan E. Lenssen, Christopher Morris +2
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph…
Quaternion Equivariant Capsule Networks for 3D Point Clouds
Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen +3
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points. The opera…
Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey, Jan Eric Lenssen
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to gene…
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey +4
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion…