We consider the problem of estimating states ( e.g., position and velocity) and physical parameters ( e.g., friction, elasticity) from a sequence of observations

Tesla is being investigated by the US government after reports the firm's self-driving cars had broken traffic laws, including driving on the wrong side of the road and not stopping for red lights. It said it was aware of 58 reports where the electric cars had committed such violations, according to a filing from the National Highway Traffic Safety Administration (NHTSA). An estimated 2.9 million cars equipped with full self-driving tech will fall under the investigation. Tesla, whose boss Elon Musk recently became the world's first half-trillionaire, has been approached for comment. The NHTSA's preliminary evaluation will assess the scope, frequency, and potential safety consequences of the Full Self-Driving (Supervised) mode.

We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence over time, and (ii) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an arbitrary monotone transformation, are well accommodated through an underlying dynamic sparse factor model. The framework naturally admits heavy-tailed innovations, capable of inferring abrupt temporal jumps in the importance of topics. Posterior inference is performed through straightforward Gibbs sampling, based on the forward-filtering backward-sampling algorithm. Moreover, an efficient data subsampling scheme is leveraged to speed up inference on massive datasets. The modeling framework is illustrated on two real datasets: the US State of the Union Address and the JSTOR collection from Science .

Graph learning from signals is a core task in graph signal processing (GSP).

Competing objectives arise when a model's capabilities

PaI methods manage to find trainable subnetworks that outperform random pruning, their performance in terms of both accuracy and computational reduction is far from satisfactory compared to post-training pruning and the understanding of PaI is missing.