Software Engineer / Backend Engineer / DevOps Engineer
based on Global and Local Searched Experiences for Neural Network Training. This thesis proposes a novel hybrid optimizer, GLAdam, which combines the benefits of meta-heuristic and gradient-based methods. GLAdam calculates the update direction by incorporating both global and local searched experiences, leading to an improved optimization process. The performance of GLAdam was evaluated through time series numerical forecasting and image classification experiments, demonstrating its effectiveness in training machine learning models. Conference paper ACM ICEA, “An Effective Optimizer based on Global and Local Searched Experiences for Short-term Electricity Consumption Forecasting”, Kor...
Full-time / Interested in working remotely
國立中山大學 National Sun Yat-Sen University
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資訊工程所