3 citations · 3 across the 5 of their papers we have counts for
6 papers
Beyond Perplexity: A Lightweight Benchmark for Knowledge Retention in Supervised Fine-Tuning
Soheil Zibakhsh Shabgahi, Pedram Aghazadeh, Farinaz Koushanfar
Supervised Fine-Tuning (SFT) is a standard approach for injecting domain knowledge into Large Language Models (LLMs). However, relying on validation perplexity to monitor training…
ForTIFAI: Fending Off Recursive Training Induced Failure for AI Model Collapse
Soheil Zibakhsh Shabgahi, Pedram Aghazadeh, Azalia Mirhoseini +1
The increasing reliance on generative AI models is rapidly increasing the volume of synthetic data, with some projections suggesting that most available new data for training could…
MergeGuard: Efficient Thwarting of Trojan Attacks in Machine Learning Models
Soheil Zibakhsh Shabgahi, Yaman Jandali, Farinaz Koushanfar
This paper proposes MergeGuard, a novel methodology for mitigation of AI Trojan attacks. Trojan attacks on AI models cause inputs embedded with triggers to be misclassified to an a…
LayerCollapse: Adaptive compression of neural networks
Soheil Zibakhsh Shabgahi, Mohammad Sohail Shariff, Farinaz Koushanfar
Handling the ever-increasing scale of contemporary deep learning and transformer-based models poses a significant challenge. Overparameterized Transformer networks outperform prior…
LiveTune: Dynamic Parameter Tuning for Feedback-Driven Optimization
Soheil Zibakhsh Shabgahi, Nojan Sheybani, Aiden Tabrizi +1
Feedback-driven optimization, such as traditional machine learning training, is a static process that lacks real-time adaptability of hyperparameters. Tuning solutions for optimiza…
Modeling Effective Lifespan of Payment Channels
Soheil Zibakhsh Shabgahi, Seyed Mahdi Hosseini, Seyed Pooya Shariatpanahi +1
While being decentralized, secure, and reliable, Bitcoin and many other blockchain-based cryptocurrencies suffer from scalability issues. One of the promising proposals to address…