Abstract: There are provided solutions for multiagent debate. In a method, a first and a second response representation are determined by a first and a second agent in a plurality of agents based on a first query representation for a query in a natural language, respectively, and the first and second response representations are convertible to a first and a second answer to the query in the natural language, respectively. A second query representation is obtained based on the first query representation, and at least one of the first and second response representations. A response representation is determined based on the second query representation by at least one of the first and second agents, and the response representation is convertible to an answer to the query in the natural language. These agents communicate in an embedding space without a conversion between a natural language format and an embedding format.

Abstract: Embodiments of the present disclosure relate to machine learning with periodic data. According to embodiments of the present disclosure, a feature representation of an input data sample is obtained from a prediction model. First Fourier coefficients for a first component in a Fourier expansion are determined by applying the feature representation into a first mapping model, and second Fourier coefficients for a second component in the Fourier expansion are determined by applying the feature representation into a second mapping model. A Fourier expansion result is determined based on the first Fourier coefficients and the second Fourier coefficients in the Fourier expansion, and a prediction result for the input data sample is determined based on the Fourier expansion result.