Time series with non-uniform intervals occur in many applications, and are difficulttomodel usingstandard recurrent neural networks(RNNs).

The technique of regularization is ubiquitous in machine learning as it can effectively prevent overfitting and yield better generalization.

We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric.

Inrecentyears,quantitativestatistical and algorithmic treatments of these formulations have produced insights into modern computational methods-resulting innovelapproaches todifficult, open problems.

Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics ofobservedsequence data.

Several papers consider approaches that limit the number of bits to represent floating point numbers [13, 24, 31]. Recent work proposes adaptive tuning of the compression ratio [7].

In addition to its use in regression, the relationship between the sinc kernel and the classic theory is illuminated, in particular, the Shannon-Nyquist theorem is interpreted as posterior reconstruction under the proposed kernel.

Despite recent advances in automated machine learning, model selection is still acomplexandcomputationally intensiveprocess.

Modern photo editing tools allow creating realistic manipulated images easily.

Converting an n-dimensional vector to a probability distribution over n objects isacommonly used component inmanymachine learning tasks likemulticlass classification,multilabelclassification,attentionmechanismsetc.