以前、集合学習や、交差検証に関する記事、また機械学習のECへの応用に関する記事等で、繰り返し、AIシステムにおけるモデルのロバストネスを高め、「過学習（Overfitting）」をいかに防いでいくかが大事であると述べました。 今回は、そもそもその「過 ...

Single-step adversarial training (SSAT) has demonstrated the potential to achieve both efficiency and robustness. However, SSAT suffers from catastrophic overfitting (CO), a phenomenon that leads to a ...

Overfitting may affect the accuracy of predicting future data because of weakened generalization. In this research, we used an electronic health records (EHR) dataset concerning breast cancer ...

Abstract: In this paper, we investigated the overfitting characteristics of nonlinear equalizers based on an artificial neural network (ANN) and the Volterra series transfer function (VSTF), which ...

Truncated BPTT is a useful technique for training language models on very long sequences. Typically a long sequences is split into chunks and a language model is trained over the chunks sequentially.

Through this post we will discuss about overfitting and methods to use to prevent the overfitting of a neural network. Everyone in the data science field is starving for a modelling procedure that can ...