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That's a really good point. We didn't, this is just SGD / EM. I will have to check if our factorized parameterization / constraints work with a spectral method.
That's great to hear that EM is promising. I think HMM and more generally latent variable models will make a comeback with a focus on unsupervised learning and uncertainty quantification. But it will be a hybrid one with function approximation from #NeuralNetworks
Our @NeurIPSConf paper introduces latent variables to model information loss in layers of feed forward #NeuralNetwork We use it to train generative feedback and show inherent robustness https://arxiv.org/abs/2007.09200 @YujiaHuangC
Thanks, I will check it out! Definitely agree that this is an interesting research area. NLP has a history of unsupervised tasks, but uncertainty quantification is under-utilized despite its importance. Hopefully more will be done in that space.
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