Event Schema Induction with Double Graph Autoencoders

no code implementations • NAACL 2022 • Xiaomeng Jin, Manling Li, Heng Ji

To induce event schemas from historical events, previous work uses an event-by-event scheme, ignoring the global structure of the entire schema graph.

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios

1 code implementation • NAACL (ACL) 2022 • Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.

Event Extraction

Chemical-Reaction-Aware Molecule Representation Learning

1 code implementation • ICLR 2022 • Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han, Martin D. Burke

Molecule representation learning (MRL) methods aim to embed molecules into a real vector space.

Chemical Reaction Prediction Property Prediction +1

On the Sensitivity of Adversarial Robustness to Input Data Distributions

no code implementations • ICLR 2019 • Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin, Luyu Wang, Ruitong Huang

Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.

Adversarial Robustness

advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

3 code implementations • 20 Feb 2019 • Gavin Weiguang Ding, Luyu Wang, Xiaomeng Jin

advertorch is a toolbox for adversarial robustness research.

Adversarial Attack Adversarial Defense +1