Recent efforts for developing general-purpose estimators with broader coverage, incorporating thefront-door adjustment (FD) (Pearl, 2000) andothers, are not scalable due to the high computational cost of summing over a highdimensional set of variables.

We introduce the Globally Normalized Autoregressive Transducer (GNAT) for addressing thelabel biasproblem instreaming speech recognition. Oursolution admits a tractable exact computation of the denominator for the sequence-level normalization.

Open Catalyst 2020 (OC20) Datasetand Community Challenges // ACSCatalysis.

We address these flaws through a study of alternative self-supervised feature extractors, find that the semantic information encoded by individual networks strongly depends on their training procedure, and show that DINOv2-ViT -L/14 allows for much richer evaluation of generative models.

Moreover,thegenerated spiketrains canfailtocapture important features of the data and even show diverging firing rates.