17 papers · ranked by Valyu relevance
Freya Acar, Ruth Seurinck, Simon B. Eickhoff, Beatrijs Moerkerke + 1 more
'Satoru Hayasaka'] The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical…
Kuniyo Sueyoshi, McAndrew Merlini, Kosuke Otsubo, Fumitsugu Kojima + 1 more
Background Early chest tube removal should be considered to enhance recovery after surgery. The current study aimed to provide a predictive algorithm for air leak episodes (ALE) and to create a knowledge base for early chest tube removal. Methods This retrospective study enrolled patients who underwent thoracoscopic…
Tommaso Costa, Donato Liloia, Franco Cauda, Peter Fox + 3 more
Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical…
Tommaso Costa, Donato Liloia, Franco Cauda, Peter T. Fox + 3 more
'Francesca Dalla Mutta' 'Sergio Duca' 'Jordi Manuello'] Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection…
Chitu Okoli
Accumulated Local Effects (ALE) is a model-agnostic approach for global explanations of the results of black-box machine learning (ML) algorithms. There are at least three challenges with conducting statistical inference based on ALE: ensuring the reliability of ALE analyses, especially in the context of small…
Lennart Frahm, Theodore D. Satterthwaite, Peter T. Fox, Robert Langner + 1 more
Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm’s behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to…
Yuki Imajuku, Horie, Kohki, Yoichi Iwata + 4 more
How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, and power-grid balancing? We introduce ALE-Bench, a new benchmark for evaluating AI systems on score-based algorithmic programming contests.…
Zhendong Shi, Xiaoli Wei, Erçan E. Kuruoğlu
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in many application scenarios. In such complicated environments, reinforcement learning (RL), a reward-oriented strategy for optimum control, has…
Kengo Shibata, Bahaaeddin Attaallah, Xin-You Tai, William Trender + 5 more
Title: Summary Background Autoimmune limbic encephalitis (ALE) is a neurological disease characterised by inflammation of the limbic regions of the brain, mediated by pathogenic autoantibodies. Because cognitive deficits persist following acute treatment of ALE, the accurate assessment of long-term cognitive outcomes…
Etinosa Osaro, Fernando Fajardo-Rojas, Gregory Cooper, Diego Gómez-Gualdrón + 1 more
Adsorption is a fundamental process studied in materials science and engineering because it plays a critical role in various applications, including gas storage and separation. Understanding and predicting gas adsorption within porous materials demands comprehensive computational simulations that are often resource…
Yuyong Tan, Jianfeng Wang, Bin Wang, Yongquan Zhou
The intelligent optimization algorithm has become a key tool in complex and intertwined engineering and science fields. However, with the increasing complexity of the problem and the rapid expansion of the data scale, the performance of the algorithm has been challenged unprecedentedly. The artificial lemming algorithm…
Kim Bongsong
Suppose that members in a universal are categorized based on observations, and that categories can be stratified based on averages of observations within each category. Two sorting extremes can be obtained from the perspective of arbitrariness of an order of observations. The first sorting extreme is an increasing…
Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour
We consider the applications of the Frank-Wolfe (FW) algorithm for Apprenticeship Learning (AL). In this setting, we are given a Markov Decision Process (MDP) without an explicit reward function. Instead, we observe an expert that acts according to some policy, and the goal is to find a policy whose feature…
Cédric Colas, Olivier Sigaud, Pierre‐Yves Oudeyer
Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep reinforcement learning. In this tutorial paper, we explain how the number of random seeds relates to the probabilities of statistical errors.…
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Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos + 5 more
A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integrate human feedback in the exploration of the chemical space to optimize molecules. A human drug designer still…
Kazunori D Yamada
In the deep learning era, a gradient descent method is the most common method to optimize parameters of neural networks. Among various mathematical optimization methods, a gradient descent method is the most naive method. Although controlling a learning rate of the method is necessary for quick convergence, the…