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Computational complexity, algorithmic scope, and evolution
Journal of Physics: ComplexityBiological systems are widely regarded as performing computations. It is much less clear, however, what exactly is computed and how biological computation fits within the framework of standard computer science.
Leonhard Sidl +7 more
semanticscholar +1 more source
North American Chapter of the Association for Computational Linguistics
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA)
Soyeong Jeong +4 more
semanticscholar +1 more source
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA)
Soyeong Jeong +4 more
semanticscholar +1 more source
OFFO minimization algorithms for second-order optimality and their complexity
Computational optimization and applications, 2022An Adagrad-inspired class of algorithms for smooth unconstrained optimization is presented in which the objective function is never evaluated and yet the gradient norms decrease at least as fast as $$\mathcal{O}(1/\sqrt{k+1})$$ O ( 1 / k + 1 ) while ...
S. Gratton, P. Toint
semanticscholar +1 more source
Annual Conference Computational Learning Theory
We study the computational and sample complexity of learning a target function $f_*:\mathbb{R}^d\to\mathbb{R}$ with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m=1}^M f_m(\langle x, v_m\rangle)$, where $f_1,f_2,...,f_M:\mathbb{R}\to ...
Kazusato Oko +3 more
semanticscholar +1 more source
We study the computational and sample complexity of learning a target function $f_*:\mathbb{R}^d\to\mathbb{R}$ with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m=1}^M f_m(\langle x, v_m\rangle)$, where $f_1,f_2,...,f_M:\mathbb{R}\to ...
Kazusato Oko +3 more
semanticscholar +1 more source
The 1982 ACM Turing Award Lecture: An Overview of Computational Complexity
Logic, Automata, and Computational Complexity, 2023S. Cook
semanticscholar +1 more source
Logic, Automata, and Computational Complexity: The Works of Stephen A. Cook
Logic, Automata, and Computational Complexity, 2023semanticscholar +1 more source
On the Sample Complexity of the Linear Quadratic Regulator
Foundations of Computational Mathematics, 2017This paper addresses the optimal control problem known as the linear quadratic regulator in the case when the dynamics are unknown. We propose a multistage procedure, called Coarse-ID control, that estimates a model from a few experimental trials ...
Sarah Dean +4 more
semanticscholar +1 more source
Randomized Complexity of Mean Computation and the Adaption Problem
Journal of ComplexityRecently the adaption problem of Information-Based Complexity (IBC) for linear problems in the randomized setting was solved in Heinrich (J. Complexity 82, 2024, 101821). Several papers treating further aspects of this problem followed.
Stefan Heinrich
semanticscholar +1 more source
A Complexity Trichotomy for k-Regular Asymmetric Spin Systems Using Number Theory
Computational Complexity, 2023Jin-Yi Cai +3 more
semanticscholar +1 more source
Current treatment and recent progress in gastric cancer
Ca-A Cancer Journal for Clinicians, 2021Smita S Joshi, Brian D Badgwell
exaly