TL;DR
The method is founded on the principle of iteratively constructing feature conjunctions that would significantly increase conditional log-likelihood if added to the model, and can be understood as supervised structure learning.
This work plants a controllable latent variable inside natural-looking text and arranges the 8 states themselves on a ring, in the exact order of the Markov chain, which is supporting evidence that a concept's geometry can be formed by the statistical dynamics of the latent variable behind it.
This paper formalise and study a succinct version of the compatibility problem, encoding conditional distributions as arithmetic circuits, and shows that, for succinct circuit representations of conditionals, the compatibility problem is intractable.
Maximum likelihood (ML) estimation is a principled and statistically efficient approach for learning probabilistic models. However, for unnormalized models, ML estimation requires evaluating the partition function and differentiating through it, which may not always be tractable. Score matching provides a practically v...
The \emph{unit} is proposed as an explicit primitive at the level of task semantics as an explicit primitive at the level of task semantics in supervised learning.
Modern semi-supervised learning (SSL) couples pseudo-label generation and classifier training, using the classifier's own confidence to select the pseudo-labels that are then used to update the model. In the cold-start regime, where at most a few labels per class are available, this coupling is ill-posed, since the cla...
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