ANN Sparse Signal Detection via Sample Weighting
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Solution Overview
Problem
Artificial neural network (ANN) models face challenges in learning to detect signals effectively when signals in data series are sparse, leading to poor learning performance or inability to learn due to mislabeled data and uneven distribution of signals.
Innovation Solution
A method that adjusts the impact of computed loss on a sample-by-sample basis by determining probability values based on the cumulative data processed and average signal frequency, generating new sample weights, and applying these weights to update the model's weights, thereby improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data science techniques are used to train the RNN, then the training process is simple and straightforward, but the model cannot effectively learn to detect signals when signals are sparse in the data series
Solution Approach 1:
The patent applies preliminary action by pre-computing probability values for each data sample before training. These probability values, which represent the likelihood that a signal is present in each sample, are calculated in advance based on signal characteristics and data distribution. This pre-computation allows the training process to incorporate signal sparsity information without adding computational complexity during the actual training execution, thereby improving signal detection accuracy while maintaining training simplicity.
2Reliability
If the RNN processes data samples with sparse signals using standard batch processing, then the processing is efficient, but the model learns from mislabeled data and cannot discriminate between target classes
Solution Approach 1:
The patent applies local quality by assigning different probability values to different data samples based on their individual characteristics. Each data sample receives a weighted probability value that reflects the local likelihood of containing a signal, rather than applying a uniform processing approach to all samples. This localized weighting ensures that samples with higher signal presence probability contribute more to the training, improving classification reliability while maintaining training efficiency through vectorized operations.
3Adaptability or versatility
If uniform distribution of signals is assumed in data samples, then the training data preparation is simple, but the model fails when signals are actually sparse and unevenly distributed
Solution Approach 1:
The patent applies dynamics by making the data processing approach adaptive rather than static. The system dynamically adjusts the probability values assigned to each data sample based on the actual signal distribution characteristics. This dynamic adjustment allows the model to adapt to various signal distributions (uniform, sparse, clustered) without requiring different processing pipelines, thereby improving data distribution adaptability while keeping the processing framework relatively simple through parameter-based flexibility.
Data Source
AI summary
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.