Philip Pham
I am currently a software engineer at Waymo, where I apply machine learning to motion planning.
Before, I worked in Google Research. My research focused on increasing model capacity and understanding inductive biases of neural networks. Natural language processing (NLP) was the main application area.
Previously at Google, I worked on internal web applications. I studied mathematics at Duke University (B.S.) and University of Pennsylvania (M.A.) and statistics at the University of Washington.
Authored Publications
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Google
OmniNet: Omnidirectional Representations from Transformers
Yi Tay
Vamsi Aribandi
ICML 2021
Long Range Arena : A Benchmark for Efficient Transformers
Yi Tay
Samira Abnar
Yikang Shen
Jinfeng Rao
Sebastian Ruder
ICLR 2021 (to appear)
ReadTwice: Reading Very Large Documents with Memories
Yury Zemlyanskiy
Joshua Ainslie
Michiel de Jong
Ilya Eckstein
Proceedings of NAACL (2021) (to appear)
Big Bird: Transformers for Longer Sequences
Manzil Zaheer
Guru Prashanth Guruganesh
Joshua Ainslie
Anirudh Ravula
Qifan Wang
Li Yang
NeurIPS (2020)
Neural Structured Learning in TensorFlow: Hands-On Tutorial at KDD
Chun-Sung Ferng
George Yu
(2020), pp. 3501-3502
ETC: Encoding Long and Structured Inputs in Transformers
Anirudh Ravula
Joshua Ainslie
Li Yang
Qifan Wang
Vaclav Cvicek
2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)
Fair Hierarchical Clustering
Benjamin Moseley
Marina Knittel
Yuyan Wang
Neurips 2020
