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Found 4 papers, 2 papers with code
Energy-efficiency Limits on Training AI Systems using Learning-in-Memory
In this paper, we derive new theoretical lower bounds on energy dissipation when training AI systems using different LIM approaches.
Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling
Our results show that the proposed approach strongly outperforms the use of backprop by up to $27\%$ higher accuracy when subject to strong hardware non-idealities.
Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets
Environmental scientists often have to predict a complex phenomenon from a heterogeneous collection of datasets.
A Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.
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