11 papers
Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
Samuel Tetteh, Udip Shrestha, Joshua R. Waite +1
Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, ins…
AI-integrated models for assessing agricultural resilience
Joshua R. Waite, Dana Golden, Brett Indelicato +8
Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems. We develop an AI-powered tool that integrates economic models (GTAP) with…
Lighting-aware Unified Model for Instance Segmentation
Qisai Liu, Alloy Das, Zhanhong Jiang +4
Foundation models like the Segment Anything Model (SAM) demonstrate impressive zero-shot generalization but frequently degrade under diverse real-world illumination, particularly f…
COOPO: Cyclic Offline-Online Policy Optimization Algorithm
Qisai Liu, Zhanhong Jiang, Joshua Russell Waite +3
Offline reinforcement learning struggles with distributional shift and constrained performance due to static dataset limitations, while online RL demands prohibitive environment in…
DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models
Nastaran Saadati, Zhanhong Jiang, Joshua R. Waite +4
Low-Rank Adaptation (LoRA) has emerged as one of the most effective, computationally tractable fine-tuning approaches for training Vision-Language Models (VLMs) and Large Language…
Bidirectional Linear Recurrent Models for Sequence-Level Multisource Fusion
Qisai Liu, Zhanhong Jiang, Joshua R. Waite +3
Sequence modeling is a critical yet challenging task with wide-ranging applications, especially in time series forecasting for domains like weather prediction, temperature monitori…
FUSE: First-Order and Second-Order Unified SynthEsis in Stochastic Optimization
Zhanhong Jiang, Md Zahid Hasan, Aditya Balu +3
Stochastic optimization methods have actively been playing a critical role in modern machine learning algorithms to deliver decent performance. While numerous works have proposed a…
Enhancing PPO with Trajectory-Aware Hybrid Policies
Qisai Liu, Zhanhong Jiang, Hsin-Jung Yang +3
Proximal policy optimization (PPO) is one of the most popular state-of-the-art on-policy algorithms that has become a standard baseline in modern reinforcement learning with applic…
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception
Joshua R. Waite, Md. Zahid Hasan, Qisai Liu +3
Vision-language model (VLM) fine-tuning for application-specific visual grounding based on natural language instructions has become one of the most popular approaches for learning-…
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
Nastaran Saadati, Minh Pham, Nasla Saleem +5
Recent advances in decentralized deep learning algorithms have demonstrated cutting-edge performance on various tasks with large pre-trained models. However, a pivotal prerequisite…
Active shooter detection and robust tracking utilizing supplemental synthetic data
Joshua R. Waite, Jiale Feng, Riley Tavassoli +4
The increasing concern surrounding gun violence in the United States has led to a focus on developing systems to improve public safety. One approach to developing such a system is…