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I am a principal research scientist at Google Deepmind, leading efforts on building conversational AI systems through RL and personalization. I received my PhD from Washington University in St. Louis, advised by Kilian Weinberger.
Minmin Chen. Research scientist, Google Deepmind. Verified email at google.com - Homepage. Machine Learning. Title. Sort. Sort by citations Sort by year Sort by title. Cited by.
Minmin Chen (2021) Preview Preview abstract How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction?
Minmin Chen is a research scientist at Google Deepmind, leading efforts in building… · Experience: Google · Location: Palo Alto · 500+ connections on LinkedIn.
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Nov 18, 2017 · We introduce MinimalRNN, a new recurrent neural network architecture that achieves comparable performance as the popular gated RNNs with a simplified structure. It employs minimal updates within RNN, which not only leads to efficient learning and testing but more importantly better interpretability and trainability.
- Minmin Chen
- arXiv:1711.06788 [stat.ML]
- 2017
Minmin CHEN | Cited by 1,823 | of Washington University in St. Louis, Missouri (WUSTL , Wash U) | Read 26 publications | Contact Minmin CHEN.
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Jun 14, 2018 · Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks. Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz. Recurrent neural networks have gained widespread use in modeling sequence data across various domains.
