Results 11 to 20 of about 573 (57)
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
, 2019 This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning.
da Silva, Leonardo Enzo Brito +2 more
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Towards an Evolvable Cancer Treatment Simulator [PDF]
, 2019 The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies. However, increasing realism comes at the cost of increasing computational requirements.
Adamatzky, Andrew +2 more
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Priors for symbolic regression
, 2023 When choosing between competing symbolic models for a data set, a human will naturally prefer the "simpler" expression or the one which more closely resembles equations previously seen in a similar context. This suggests a non-uniform prior on functions,
Bartlett, Deaglan J. +2 more
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Analysis and FPGA based Implementation of Permutation Binary Neural Networks
, 2023 This paper studies a permutation binary neural network characterized by local binary connections, global permutation connections, and the signum activation function.
Onuki, Mikito +2 more
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Deep Learning: Our Miraculous Year 1990-1991
, 2020 In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich.
Schmidhuber, Juergen
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Optimization of a Hydrodynamic Computational Reservoir through Evolution
, 2023 As demand for computational resources reaches unprecedented levels, research is expanding into the use of complex material substrates for computing. In this study, we interface with a model of a hydrodynamic system, under development by a startup, as a ...
Heiney, Kristine +4 more
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Disclosure of a Neuromorphic Starter Kit
, 2022 This paper presents a Neuromorphic Starter Kit, which has been designed to help a variety of research groups perform research, exploration and real-world demonstrations of brain-based, neuromorphic processors and hardware environments.
Foshie, Adam Z. +4 more
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Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient
, 2022 A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection.
Chen, Yan +4 more
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Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning [PDF]
, 2022 Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in the form of genetic programming (GP), represents a key enabler for the ...
Alderliesten, Tanja +3 more
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EXPERIMENTS IN THE USE OF ΔΔ PREDICTIONS FOR DENOVO IN-SILICO PROTEIN DESIGN [PDF]
"The biosphere solution to the protein design problem is akin to a planetwide computational machine running a simple stochastic algorithm. The root of its success lies in the open endedness and diversity of its search, willing to extend across all ...
Bernardino, Rodrigo António Correia Tavares
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