Search alternatives:
latest decrease » largest decrease (Expand Search), greatest decrease (Expand Search), largest decreases (Expand Search)
larger decrease » marked decrease (Expand Search)
task decrease » a decrease (Expand Search), ash decreased (Expand Search)
teer decrease » mean decrease (Expand Search), greater decrease (Expand Search)
latest decrease » largest decrease (Expand Search), greatest decrease (Expand Search), largest decreases (Expand Search)
larger decrease » marked decrease (Expand Search)
task decrease » a decrease (Expand Search), ash decreased (Expand Search)
teer decrease » mean decrease (Expand Search), greater decrease (Expand Search)
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Biases in larger populations.
Published 2025“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”
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Network architectures for multi-agents task.
Published 2025“…<div><p>Deep reinforcement learning has achieved significant success in complex decision-making tasks. However, the high computational cost of policies based on deep neural networks restricts their practical application. …”
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Time(s) and GFLOPs savings of single-agent tasks.
Published 2025“…<div><p>Deep reinforcement learning has achieved significant success in complex decision-making tasks. However, the high computational cost of policies based on deep neural networks restricts their practical application. …”
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Scores vs Skip ratios on single-agent task.
Published 2025“…<div><p>Deep reinforcement learning has achieved significant success in complex decision-making tasks. However, the high computational cost of policies based on deep neural networks restricts their practical application. …”
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Win rate vs Skip ratios on multi-agents tasks.
Published 2025“…<div><p>Deep reinforcement learning has achieved significant success in complex decision-making tasks. However, the high computational cost of policies based on deep neural networks restricts their practical application. …”
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Single agent and multi-agents tasks for <i>LazyAct</i>.
Published 2025“…<div><p>Deep reinforcement learning has achieved significant success in complex decision-making tasks. However, the high computational cost of policies based on deep neural networks restricts their practical application. …”
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Model-derived results show increased social following in individuals with disrupted utility-based risky decision-making.
Published 2024“…<p><b>(a)</b> Parameter estimates across all trials showed that individuals with insula or dACC lesions had significantly larger ω<sub>follow</sub> estimates than non-lesioned control participants (NC; NC vs insula: <i>P</i> = 0.036; NC vs dACC: <i>P</i> = 0.025; dACC vs insula: <i>P</i> = 0.89, BF<sub>null</sub> = 2.28), indicating that the lesion participants were more likely to conform with others’ choices during decision-making in a social context. …”
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The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness.
Published 2025“…Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. …”
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