… probabilistic recurrent state-space model1 (PRSSM2). PR-SSM takes inspiration from RNN
model … inference scheme for learning probabilistic, Markovian state-space models. Based on …

… As baselines, we compare to Recurrent State Space Models [23] and the Variational … In
order to learn the state space model from data and use it for downstream RL, we need to infer …

… Benefiting from the significant progress of Deep Neural Networks, such as LSTM) and
DeepAR [7] employs an auto-regressive recurrent network model to predict probabilistic …

… -time state-space representation $\dot {x}= Ax+ Bu, y= Cx+ Du $. Theoretically, we show that
LSSL models are closely related to the three aforementioned families of models and inherit …

… We propose a deep state space model, for the term deep it is reflected in two aspects: i) We
… non-linear emission model and transition model; ii) We use recurrent neural networks (RNNs…

… networks are recurrent. Inferring the posterior probabilities of the hidden states of this model
is … To this effect, we introduce a probabilistic model called the switching state-space model …

… connections to existing statistical models such as state space models and hidden Markov …
develop a probabilistic graphical model that is induced by a generative model formulation of …

… to probabilistic time series forecasting that combines state space models with deep learning…
By parametrizing a per-time-series linear state space model with a jointly-learned recurrent …

… Deep State Space Models (SSMs) are effective for identifying systems in the latent state space,
… Generative Model As a conditional probabilistic model, the generative process describes …

… the resulting probabilistic recurrent … state space model that learns to account for the causal
factors of variation observed across tasks at training time, and then infer at test time the model …