3 citations · 5 across the 9 of their papers we have counts for
9 papers
CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
Chung-En Johnny Yu, David Garcia, Brian Jalaian +1
Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures re…
A Generalized Optimization Engine (GOE) for Edge AI Inference Acceleration
Venkat R. Dasari, Jakob A. Adams, Vinod K. Mishra +1
Artificial intelligence (AI) models have demonstrated remarkable capabilities across various domains, yet their widespread deployment is impeded by significant computational costs,…
SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs amplifies uncertainty and increases…
Advancing Model Refinement: Muon-Optimized Distillation and Quantization for LLM Deployment
Jacob Sander, Brian Jalaian, Venkat R. Dasari
Large Language Models (LLMs) enable advanced natural language processing but face deployment challenges on resource-constrained edge devices due to high computational, memory, and…
Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inference typically provides; yet, retraining…
ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Large Vision-Language Models (LVLMs) exhibit strong multimodal capabilities but remain vulnerable to hallucinations from intrinsic errors and adversarial attacks from external expl…
Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression
Jacob Sander, David Moe, Achraf Cohen +3
Modern foundational models are often compressed via a combination of structured pruning and re-training to meet the strict compute, memory, and connectivity constraints of edge dep…
On Accelerating Edge AI: Optimizing Resource-Constrained Environments
Jacob Sander, Achraf Cohen, Venkat R. Dasari +2
Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a compreh…
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI
Alice Bizzarri, Chung-En Yu, Brian Jalaian +2
The prevailing approaches in Network Intrusion Detection Systems (NIDS) are often hampered by issues such as high resource consumption, significant computational demands, and poor…