Results 11 to 20 of about 7,806,329 (300)
Machine Learned Learning Machines
CoRR, 2017 There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary
Leigh Sheneman, Arend Hintze
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Exploring and Exploiting Conditioning of Reinforcement Learning Agents
IEEE Access, 2020 The outcome of Jacobian singular values regularization was studied for supervised learning problems. In supervised learning settings for linear and nonlinear networks, Jacobian regularization allows for faster learning.
Arip Asadulaev +3 more
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Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
Arthritis Research & Therapy, 2022 Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has ...
Alix Bird +8 more
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F1000Research, 2022 Science is justly admired as a cumulative process (“standing on the shoulders of giants”), yet scientific knowledge is typically built on a patchwork of research contributions without much coordination.
Judith ter Schure, Peter Grünwald
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Greedy structure learning from data that contain systematic missing values [PDF]
, 2022 Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume ...
Liu, Y +5 more
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Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets
Complexity, 2021 K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work presents
Stanislav Protasov, Adil Mehmood Khan
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A Review of Causality for Learning Algorithms in Medical Image Analysis
, 2022 Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area.
Vlontzos, Athanasios +2 more
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Distributional Prototypical Methods for Reliable Explanation Space Construction
IEEE Access, 2023 As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas.
Hyungjun Joo +3 more
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Quantum-chemical insights from deep tensor neural networks
Nature Communications, 2017 Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical ...
Kristof T. Schütt +4 more
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Learning labelled dependencies in machine translation evaluation [PDF]
, 2009 Recently novel MT evaluation metrics have been presented which go beyond pure string matching, and which correlate better than other existing metrics with human judgements.
He, Yifan, Way, Andy
core +2 more sources