Secondly, it analyses the role of a specific and relatively new CAP measure (i. e., the Income Stabilisation Tool - IST) that is specifically aimed at stabilising farm income.
The design and performance of computer vision algorithms are greatly influenced by the hardware on which they are implemented.
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data.
This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs.
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state.
Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers.
We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle.
The number of parameters describing a quantum state is well known to grow exponentially with the number of particles.
This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA).
This suggests that the probability distributions associated to hard quantum states might have a compositional structure that can be exploited by layered neural networks.
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.