Abstract: This disclosure enables various computing technologies for various data science techniques for ameliorating negative impacts of signals that are sparse in various data series for trainings of ANN models. These data science techniques can be helpful for dealing with time series or non-fixed-length time spans or other forms of discretized, parsed, or tokenized data. Some of the data science techniques can enable a process that ameliorates a negative impact of a sparse signal on a learning performance of an ANN model. This amelioration can occur by adjusting an impact of a computed loss on a learning process of an ANN on a sample-by-sample basis in such a way as to reflect a probability that the ANN model has seen a signal for that sample.

Abstract: This disclosure enables various computing technologies for various data science techniques to preserve signals in data inputs with moderate to high levels of variances in data sequence lengths for artificial neural network model training. These data science techniques can be helpful for dealing with time series or non-fixed-length time spans or other forms of discretized, parsed, or tokenized data. Some technological effects of utilizing these data science techniques can be technologically equivalent to getting more relevant training data, which can allow a model of a neural network to be trained to a high level of accuracy.