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What it Thinks is Important is Important: Robustness Transfers through Input Gradients., , and . CoRR, (2019)Holographic Factorization Machines for Recommendation., , , , , and . AAAI, page 5143-5150. AAAI Press, (2019)Compositional De-Attention Networks., , , , and . NeurIPS, page 6132-6142. (2019)Robust Representation Learning of Biomedical Names., , and . ACL (1), page 3275-3285. Association for Computational Linguistics, (2019)The Efficiency Misnomer., , , , and . ICLR, OpenReview.net, (2022)How Reliable are Model Diagnostics?, , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 1778-1785. Association for Computational Linguistics, (2021)Cross-Device User Linking: URL, Session, Visiting Time, and Device-log Embedding., , and . SIGIR, page 933-936. ACM, (2017)Multi-Cast Attention Networks., , and . KDD, page 2299-2308. ACM, (2018)Holistic Multi-modal Memory Network for Movie Question Answering., , , , , and . CoRR, (2018)Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?, , , , , , , , , and . EMNLP (Findings), page 12342-12364. Association for Computational Linguistics, (2023)