110 citations · 234 across the 12 of their papers we have counts for
5 papers · 1 filter
Labeled Interactive Topic Models
Kyle Seelman, Mozhi Zhang, Jordan Boyd-Graber
Topic models are valuable for understanding extensive document collections, but they don't always identify the most relevant topics. Classical probabilistic and anchor-based topic…
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka +1
Graph Neural Networks (GNNs) have been studied through the lens of expressive power and generalization. However, their optimization properties are less well understood. We take the…
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?
Jingling Li, Mozhi Zhang, Keyulu Xu +2
Noisy labels are inevitable in large real-world datasets. In this work, we explore an area understudied by previous works -- how the network's architecture impacts its robustness t…
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu, Mozhi Zhang, Jingling Li +3
We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical…
What Can Neural Networks Reason About?
Keyulu Xu, Jingling Li, Mozhi Zhang +3
Neural networks have succeeded in many reasoning tasks. Empirically, these tasks require specialized network structures, e.g., Graph Neural Networks (GNNs) perform well on many suc…