12 citations · 30 across the 5 of their papers we have counts for
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cs.LG2020★ 2 cited
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
Cheng Wang, Carolin Lawrence, Mathias Niepert
Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stoch…
cs.LG2019★ 10 cited
State-Regularized Recurrent Neural Networks
Cheng Wang, Mathias Niepert
Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Secon…
cs.LG2017★ 12 cited
RRA: Recurrent Residual Attention for Sequence Learning
Cheng Wang
In this paper, we propose a recurrent neural network (RNN) with residual attention (RRA) to learn long-range dependencies from sequential data. We propose to add residual connectio…