Active Target Discovery under Uninformative Prior: The Power of Permanent and Transient Memory

2025-10-18

AI Summary

In high-cost sampling domains—such as medical imaging and remote sensing—active target discovery remains challenging due to scarce prior knowledge. To address this, we propose a neuroscience-inspired neural framework that operates without strong priors. Our method introduces a permanent–transient dual-memory mechanism for hierarchical modeling of observational history; designs an interpretable, monotonically optimizing prior estimation architecture that eliminates reliance on initial priors inherent in generative models; and integrates diffusion-model principles with sequential active learning to dynamically refine sampling strategies under low-data-density conditions. Evaluated on species distribution modeling and remote sensing detection tasks, our approach consistently outperforms state-of-the-art baselines, demonstrating superior efficiency, robustness, and cross-domain generalizability.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

In many scientific and engineering fields, where acquiring high-quality data is expensive--such as medical imaging, environmental monitoring, and remote sensing--strategic sampling of unobserved regions based on prior observations is crucial for maximizing discovery rates within a constrained budget. The rise of powerful generative models, such as diffusion models, has enabled active target discovery in partially observable environments by leveraging learned priors--probabilistic representations that capture underlying structure from data. With guidance from sequentially gathered task-specific observations, these models can progressively refine exploration and efficiently direct queries toward promising regions. However, in domains where learning a strong prior is infeasible due to extremely limited data or high sampling cost (such as rare species discovery, diagnostics for emerging diseases, etc.), these methods struggle to generalize. To overcome this limitation, we propose a novel approach that enables effective active target discovery even in settings with uninformative priors, ensuring robust exploration and adaptability in complex real-world scenarios. Our framework is theoretically principled and draws inspiration from neuroscience to guide its design. Unlike black-box policies, our approach is inherently interpretable, providing clear insights into decision-making. Furthermore, it guarantees a strong, monotonic improvement in prior estimates with each new observation, leading to increasingly accurate sampling and reinforcing both reliability and adaptability in dynamic settings. Through comprehensive experiments and ablation studies across various domains, including species distribution modeling and remote sensing, we demonstrate that our method substantially outperforms baseline approaches.

Problem

• Enables active target discovery with uninformative priors in data-scarce domains
• Ensures robust exploration and adaptability in complex real-world scenarios
• Provides interpretable decision-making with guaranteed monotonic improvement in estimates

Innovation

• Combines permanent and transient memory mechanisms
• Ensures monotonic improvement in prior estimates
• Provides interpretable decision-making for exploration