2 citations · 2 across the 4 of their papers we have counts for
6 papers
Looped GNNs Beyond Fixed Depth: Graph Size Generalization and Parameter-Efficient Adaptation
Zhiyang Wang, Zhishang Luo, Yusu Wang
Generalizing from small graphs to larger ones can require computation beyond the depth of a neural model used during training. We study target tasks that admit a common local itera…
Size-Transferability of Graph Transformers with Convolutional Positional Encodings
Javier Porras-Valenzuela, Zhiyang Wang, Xiaotao Shang +3
Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key desi…
Which Algorithms Can Graph Neural Networks Learn?
Solveig Wittig, Antonis Vasileiou, Robert R. Nerem +4
In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a line of work often referred to as neural…
Graph neural networks extrapolate out-of-distribution for shortest paths
Robert R. Nerem, Samantha Chen, Sanjoy Dasgupta +1
Neural networks (NNs), despite their success and wide adoption, still struggle to extrapolate out-of-distribution (OOD), i.e., to inputs that are not well-represented by their trai…
Differentiable Extensions with Rounding Guarantees for Combinatorial Optimization over Permutations
Robert R. Nerem, Zhishang Luo, Akbar Rafiey +1
Continuously extending combinatorial optimization objectives is a powerful technique commonly applied to the optimization of set functions. However, few such methods exist for exte…
NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem
Andrew B. Kahng, Robert R. Nerem, Yusu Wang +1
Recent years have witnessed rapid advances in the use of neural networks to solve combinatorial optimization problems. Nevertheless, designing the "right" neural model that can eff…