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A Probabilistic Semi-Supervised Approach with Triplet Markov Chains
Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete labels and latent variables which aim at strengthening the distribution of the observations and their associated labels.
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint
The probability distributions produced by these models are characterized by structured covariance series, and as a consequence expressivity reduces to comparing sets of structured covariance series, which enables us to call for stochastic realization theory (SRT).
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
To achieve this, we present a new centralized distributed learning algorithm that relies on the learning paradigms of Active Learning and Federated Learning to offer a communication-efficient method that offers guarantees of model precision on both the clients and the central server.
