Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive ...
computational complexity offers outstanding research at the junction of mathematics and theoretical computer science. Covers models of computation, ...
Most state-of-the-art nonparametric machine learning algorithms have a computational complexity of either O(N 2 ) or O(N 3 ), where N is the number of training examples. In my thesis I propose to explore the scalability of machine learning algorithms both with respect to the number of ...
Computational complexity increased difficulty and promoted satisficing behavior In nearly all 462 instances, the animals performed better than predicted by chance, yet there was significant performance variability between instances (Fig. 2a,b). We sought to determine whether computational complexity modulated...
(bilinear complexity, computations for polynomials, groups, algebras, and representations) Interactive proofs, pseudorandom generation, and randomness extraction Complexity issues in: cryptography learning theory number theory logic (complexity of logical theories, cost of decision procedures) combinatorial ...
interested in computational complexity theory, especially because of high-profile re- sults such as Shor’s algorithm and the recent deterministic test for primality. This vi intellectually sophisticated group will be able to quickly read through Part I. Progress- ...
one measures the complexity of learning either by a single number/ordinal for the whole class or by a function in a parameterndescribing the complexity of the target to be learned. The actual measure can be the number of mind changes, the number of queries submitted to a teacher, the numbe...
Pre-Publication Review: "This text is a major achievement that brings together all of the important developments in complexity theory. Student and researchers alike will find it to be an immensely useful resource." Michael Sipser, MIT, author of Introduction to the Theory of Computation Pre-Public...
On the Computational Complexity of Optimal Multisplitting.Presents a study that examined the computational complexity of the multisplitting problem in machine learning. Background on machine learning; Examination of optimal multisplitting problems and the monotonicity formulations that have emerged in string...
and gradient calculations. FLOPs are often used to measure the computational cost or complexity of a model or a specific operation within a model. This is helpful when we need to provide an estimation of the total arithmetic operations required, which is generally used in the context of measurin...