Abstract
Hyperparameters are pivotal for machine learning models. The success of efficient calibration, often surpasses the results obtained by devising new approaches. Traditionally, human intervention is required to tune the models, however, this obtuse outlook restricts the proficiency and competence. Automating this crucial characteristic of learning sustainably, proffers a significant boost in performance and cost optimization. Blockchain technology has revolutionized industries utilizing its Proof-of-Work algorithms for consensus. This complicated solution generates a lot of useless computations across the nodes attached to the network and thus, fritters away a huge amount of precious energy. In this paper, we propose to exploit these inane computations for training deep learning models instead of calculating purposeless hash values, thus, suggesting a new consensus schema. This work distinguishes itself from other related works by capitalizing on the parallel processing prospects it generates for hyperparameter tuning of complex deep learning models. We address this aspect through the framework of Bayesian optimization which is an effective methodology for the global optimization of functions with expensive evaluations. We call our work, Proof of Deep Learning with Hyperparameter Optimization (PoDLwHO).
Highlights
The accelerating ubiquity of machine learning has radically altered the way technology caters to human needs (LeCun et al, 2015)
We can conclude that over the course of our experiment, we found that the accuracy of the best model received so far has improved after a period of 2-3 block generations
Another thing to note is that the sample points for hyperparameters set are not biased toward a local neighborhood, rather, the Bayesian optimization (BO) ensures a careful balance of exploration and exploitation
Summary
The accelerating ubiquity of machine learning has radically altered the way technology caters to human needs (LeCun et al, 2015). Deep learning networks have steered their way into human lives, complementing their capabilities, in the form of voice assistants (Hoy, 2018), targeted advertisements (Perlich et al, 2014), recommendation services (Linden et al, 2003) and life crucial applications like cancer prognosis (Kourou et al, 2015). Numerous endeavors have been undertaken to optimize hyperparameters like racing algorithms (Maron and Moore, 1994), gradient search (Bengio, 2000) and random search (Bergstra and Bengio, 2012), each ameliorating the performance and operational efficiency. Bayesian optimization (BO), a sophisticated global optimization algorithm, has exhibited several interesting characteristics rendering it suitable for automated exploration in the myriad of hyperparameter choices. It has outperformed the traditional methods and is considereda huge step toward
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