Design of an basis-projected layer for sparse datasets in deep learning training using gc-ms spectra as a case study

arXiv:2403.09188v1 Announce Type: new
Abstract: Deep learning (DL) models encompass millions or even billions of parameters and learn complex patterns from big data. However, not all data are initially stored in a suitable formation to effectively train a DL model, e.g., gas chromatography-mass spectrometry (GC-MS) spectra and DNA sequence. These datasets commonly contain many zero values, and the sparse data formation causes difficulties in optimizing DL models. A DL module called the basis-projected layer (BPL) was proposed to mitigate the …

abstract arxiv big big data case case study cs.lg data datasets deep learning deep learning training design however layer learn parameters patterns study train training type