10 papers · 1 filter
Neural CDEs as Correctors for Learned Time Series Models
Muhammad Bilal Shahid, Zhanhong Jiang, Prajwal Koirala +2
Learned time-series models, whether continuous or discrete, are widely used for forecasting the states of dynamical systems but suffer from error accumulation in multi-step forecas…
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati +3
Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model paramet…
Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
Zhanhong Jiang, Aditya Balu, Soumik Sarkar
-smoothness, which has been pivotal to advancing decentralized optimization theory, is often fairly restrictive for modern tasks like deep learning. The recent advent of relax…
Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Zhanhong Jiang, Dylan Shah, Hsin-Jung Yang +1
Precise kinematic modeling is critical in calibration and controller design for soft robots, yet remains a challenging issue due to their highly nonlinear and complex behaviors. To…
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…
SC-NeRF: NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications
Kibon Ku, Talukder Z Jubery, Elijah Rodriguez +4
This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-bas…
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-…