1 citations · 1 across the 10 of their papers we have counts for
12 papers
LSA-PAST: Layer-Specific Adaptation and Prototype Transport for Compositional Incremental Learning
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +2
Class-Incremental Learning (CIL) enables models to recognize new classes over time while retaining prior knowledge, but many real-world problems are compositional, where visual con…
From Task Mixtures to Specialized Experts
Hojat Allah Salehi, Mehrdad Mahdavi, Andrew Arash Mahyari +1
In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data distributions, or tasks. Conv…
Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition
Kyle Stein, Guillermo Francia, III Eman El-Sheikh +1
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on ne…
Curvature-Guided Module Localization for Low-Rank Detoxification of Backdoored Large Language Models
Arash Raftari, Mehrdad Mahdavi, Nathan Blackthorn +1
Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious behavior when a hidden trigger is…
Proactive Disentangled Modeling of Trigger-Object Pairings for Backdoor Defense
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify o…
Transductive One-Shot Learning Meet Subspace Decomposition
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
One-shot learning focuses on adapting pretrained models to recognize newly introduced and unseen classes based on a single labeled image. While variations of few-shot and zero-shot…
Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +1
Integrating new class information without losing previously acquired knowledge remains a central challenge in artificial intelligence, often referred to as catastrophic forgetting.…
Proactive Adversarial Defense: Harnessing Prompt Tuning in Vision-Language Models to Detect Unseen Backdoored Images
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +1
Backdoor attacks pose a critical threat by embedding hidden triggers into inputs, causing models to misclassify them into target labels. While extensive research has focused on mit…
Toward a Real-Time Digital Twin Framework for Infection Mitigation During Air Travel
Ashok Srinivasan, Satkkeerthi Sriram, Sirish Namilae +1
Pedestrian dynamics simulates the fine-scaled trajectories of individuals in a crowd. It has been used to suggest public health interventions to reduce infection risk in important…
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
As the complexity and connectivity of networks increase, the need for novel malware detection approaches becomes imperative. Traditional security defenses are becoming less effecti…
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples
Kyle Stein, Arash Mahyari, Guillermo Francia +1
As networks continue to expand and become more interconnected, the need for novel malware detection methods becomes more pronounced. Traditional security measures are increasingly…
Harnessing the Power of LLMs in Source Code Vulnerability Detection
Andrew A Mahyari
Software vulnerabilities, caused by unintentional flaws in source code, are a primary root cause of cyberattacks. Static analysis of source code has been widely used to detect thes…