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Computer science

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Formal Foundations of Computation

1923 - 1952

During this period the dominant paradigm was formalization of computation itself, unifying lambda calculus, recursive functions, and logic into rigorous models that could address decision problems and the interpretation of computation across formal systems. Concurrently, functional-analytic and algebraic methods provided the substrate for computation, while geometry and topology offered computational reasoning tools that connected spatial and topological structure to logical and algorithmic processes.

Formal foundations of computation unify lambda calculus, recursive functions, and logic, addressing decision problems, completeness, and interpretation across formal systems. [1] [13] [14] [18] [6]

Functional-analytic machinery, including integration in Banach/vector spaces and operator-theoretic results, serves as rigorous substrate for aggregation, convergence, and computation. [2] [11] [10] [20] [8]

Geometry and topology provide computational reasoning tools via cohomology, map coloring, and semi-locally connected sets, linking topology to computation and reasoning. [4] [7] [9] [3]

Algebraic structures and representations underpin informally the modeling of logic and computation, including representation for Boolean algebras and foundational logical postulates. [5] [14] [18]

Optimization Under Uncertainty

1953 - 1965

Foundations of Algorithmic Computing

1966 - 1972

Integrated Information Processing Foundations

1973 - 1979

Neural Computation Foundations

1980 - 1986

Wavelet-Driven Multiresolution Learning

1987 - 1993

Representational and Kernel Learning

1994 - 2000

Bag-of-Features Era

2001 - 2010

Deep Convolutional Networks Era

2011 - 2017

Efficient Attention-Driven Learning

2018 - 2024