activity
20182021
most citedOptimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth

16 citations · 17 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL2021

A Dataset and Baselines for Multilingual Reply Suggestion

Mozhi Zhang, Wei Wang, Budhaditya Deb +3

Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion da…

cs.LG202116 cited

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…

cs.LG2020

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…

cs.CL20201 cited

Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries

Mozhi Zhang, Yoshinari Fujinuma, Michael J. Paul +1

Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI). Recent CLWE methods use linear projections, which underfit the training dictionary, t…

cs.CL2019

Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization

Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi +2

Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.…

cs.LG2019

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…