2.1k citations · 8.9k across the 84 of their papers we have counts for
7 papers · 1 filter
What it Thinks is Important is Important: Robustness Transfers through Input Gradients
Alvin Chan, Yi Tay, Yew-Soon Ong
Adversarial perturbations are imperceptible changes to input pixels that can change the prediction of deep learning models. Learned weights of models robust to such perturbations a…
Interactive Machine Comprehension with Information Seeking Agents
Xingdi Yuan, Jie Fu, Marc-Alexandre Cote +3
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue t…
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Yi Tay, Aston Zhang, Luu Anh Tuan +5
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architect…
Quaternion Collaborative Filtering for Recommendation
Shuai Zhang, Lina Yao, Lucas Vinh Tran +2
This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Qu…
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
Yi Tay, Shuohang Wang, Luu Anh Tuan +6
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Point…
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
Shuai Zhang, Yi Tay, Lina Yao +2
Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both resear…