Publications associées à Computational complexity

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Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Computational complexity

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Graph generative deep learning models with an application to circuit topologies

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Efficient Continual Finite-Sum Minimization

Inhalation of Microplastics—A Toxicological Complexity

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Graph generative deep learning models with an application to circuit topologies

Efficient Continual Finite-Sum Minimization

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Inhalation of Microplastics—A Toxicological Complexity

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency