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Full-Text Articles in Physical Sciences and Mathematics
Creating Interpretable Deep Learning Models To Identify Species Using Environmental Dna Sequences, Samuel Waggoner
Creating Interpretable Deep Learning Models To Identify Species Using Environmental Dna Sequences, Samuel Waggoner
Honors College
This research aims to develop an interpretable and fast machine learning (ML) model for identifying species using environmental DNA (eDNA). eDNA is a technique used to detect the presence or absence of species in an ecosystem by analyzing the DNA that animals naturally leave behind in water or soil. However, there can be millions of sequences to classify and the reference databases are sizeable, so traditional methods such as BLAST are slow. Convolutional neural networks (CNNs) have been shown to be 150 times faster at classifying sequences. In this work, we create a CNN that achieves 92.5% accuracy, surpassing the …
Ghost Connect-Net: A Connectivity-Based Companion Network To Enhance Pruning Methods, Mary Isabelle Wisell
Ghost Connect-Net: A Connectivity-Based Companion Network To Enhance Pruning Methods, Mary Isabelle Wisell
Honors College
Deep neural network (DNN) approaches excel in various real-world applications like robotics and computer vision, yet their computational demands and memory requirements hinder usability on advanced devices. Also, larger models heighten overparameterization risks, making networks more vulnerable to input disturbances. Recent studies aim to boost DNN efficiency by trimming redundant neurons or filters based on task relevance. Instead of introducing a new pruning method, this project aims to enhance existing techniques by introducing a companion network, Ghost Connect-Net (GC-Net), to monitor the connections in the original network. The initial weights of GC- Net are equal to the connectivity measurements of …