main webpage W Topic Heuristic arXiv (Cornell University) Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help? 2019 The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning strategies performed over distinct discrete architecture choices. In the latter… Article Open Heuristic Open Article Page

Heuristic

Problem-solving method that is sufficient for immediate solutions or approximations A heuristic (; from Ancient Greek __ εὑρίσκω ( _heurískō )_ 'to find, discover'), or heuristic technique , is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation in a search space. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Examples that employ heuristics include using trial and error, a rule of thumb or an educated guess. Critical Symbolic Virtual Narrative Open Heuristic Open Article Page

Exploring foci of: arXiv (Cornell University) Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help? 2019 The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning strategies performed over distinct discrete architecture choices. In the latter case, the optimization is often non-differentiable and also not very amenable to derivative-free optimization methods. Most methods in use today require sizable computational resources. And if we want networks that additionally satisfy resource constraints, t… Open Article Page

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Explore Heuristic W Topic Heuristic arXiv (Cornell University) Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help? 2019 The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning strategies performed over distinct discrete architecture choices. In the latter… Article Critical Symbolic Virtual Narrative Open Heuristic Open Article Page