2 citations · 3 across the 11 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
On the Effect of Bit-Level Parameter Perturbations in Machine Learning and Deep Learning Models
Akanksha Raghapur, Mark Stamp
In this chapter, we investigate how classical machine learning models respond to small, targeted modifications in their parameters. We compare and contrast these results to analogo…
cs.LG2025
Energy Considerations for Large Pretrained Neural Networks
Leo Mei, Mark Stamp
Increasingly complex neural network architectures have achieved phenomenal performance. However, these complex models require massive computational resources that consume substanti…
cs.LG2024
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network
Ritik Mehta, Olha Jureckova, Mark Stamp
The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of…