Arrow Research search

Author name cluster

Amin Ullah

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
2 author rows

Possible papers

2

NeurIPS Conference 2025 Conference Paper

Convex Potential Mirror Langevin Algorithm for Efficient Sampling of Energy-Based Models

  • Zitao Yang
  • Amin Ullah
  • Shuai Li
  • Fuxin Li
  • Jun Li

This paper introduces the Convex Potential Mirror Langevin Algorithm (CPMLA), a novel method to improve sampling efficiency for Energy-Based Models (EBMs). CPMLA uses mirror Langevin dynamics with a convex potential flow as a dynamic mirror map for EBM sampling. This dynamic mirror map enables targeted geometric exploration on the data manifold, accelerating convergence to the target distribution. Theoretical analysis proves that CPMLA achieves exponential convergence with vanishing bias under relaxed log-concave conditions, supporting its efficiency in adapting to complex data distributions. Experiments on benchmarks like CIFAR-10, SVHN, and CelebA demonstrate CPMLA's improved sampling quality and inference efficiency over existing techniques.

PDF Details

ICRA Conference 2024 Conference Paper

CVAE-SM: A Conditional Variational Autoencoder with Style Modulation for Efficient Uncertainty Quantification

  • Amin Ullah
  • Taiqing Yan
  • Fuxin Li

Deep learning has brought transformative advancements to object segmentation, especially in marine robotics contexts such as waste management and subaquatic infrastructure oversight. However, a central challenge persists: calibrating the prediction confidence of the model to ensure robust and reliable outcomes, especially within the demanding underwater environment. Existing solutions for estimating uncertainty are often computationally intensive and have largely centered around Bayesian neural networks or ensemble methods. In this paper, we present a Conditional Variational Autoencoder-based framework (CVAE-SM), which is capable of generating diverse latent codes for improved uncertainty quantification in image segmentation. Our method, enhanced by a style modulator, merges content features, and latent codes more effectively, leading to refined prediction of uncertainty levels. We further introduce a dataset of perturbed underwater images to benchmark uncertainty quantification in this domain. The proposed model not only surpasses peers in segmentation metrics but also matches ensemble models in uncertainty predictions, all while being 2. 5 times faster.

Details