Discovering and forecasting extreme events via active learning in neural operators by Pickering, Ethan, Guth, Stephen, Karniadakis, George Em, Sapsis, Themistoklis P

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Bayesian neural network language modeling for speech recognition by Xue, Boyang, Hu, Shoukang, Xu, Junhao, Geng, Mengzhe, Liu, Xunying, Meng, Helen

“…In particular, on the LRS2 data, statistically significant WER reductions up to 1.3% and 1.2% absolute (12.1% and 11.3% relative) were obtained over the baseline LSTM-RNN and Transformer LMs respectively after model combination between Bayesian NNLMs and their respective baselines.…”
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Probabilistic Programming with Programmable Variational Inference by Becker, McCoy R., Lew, Alexander K., Wang, Xiaoyan, Ghavami, Matin, Huot, Mathieu, Rinard, Martin C., Mansinghka, Vikash K.

“…Compared to the wide array of advanced Monte Carlo methods supported by modern probabilistic programming languages (PPLs), PPL support for variational inference (VI) is underdeveloped: users are typically limited to a small selection of predefined variational objectives and gradient estimators, which are implemented monolithically (and without explicit correctness arguments) in PPL backends. …”
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