6 papers
Environment-Conditioned Diffusion Meta-Learning for Data-Efficient WiFi Localization
Jun Gao, Zheng Xing, Wenliang Lin +5
Fingerprinting-based localization often suffers from poor cross-environment generalization, especially when only a few labeled samples are available in the target environment. Exis…
MinT: Managed Infrastructure for Training and Serving Millions of LLMs
Mind Lab, :, Song Cao +60
We present MindLab Toolkit (MinT), a managed infrastructure system for Low-Rank Adaptation (LoRA) post-training and online serving. MinT targets a setting where many trained polici…
LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
Lanxin Zhao, Bamdev Mishra, Pratik Jawanpuria +4
Orthogonal parameter-efficient fine-tuning (PEFT) adapts pretrained weights through structure-preserving multiplicative transformations, but existing methods often conflate two dis…
Annotation-Free Indoor Radio Mapping via Physics-Informed Trajectory Inference
Zheng Xing, Mengru Wu, Yi Zhang +6
Constructing indoor radio maps traditionally requires extensive site surveys with precise user-location labels, making the calibration process costly and time-consuming. Existing c…
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints
Wenzhong Yan, Feng Yin, Jun Gao +3
Fingerprint-based indoor localization is often labor-intensive due to the need for dense grids and repeated measurements across time and space. Maintaining high localization accura…
GenMetaLoc: Learning to Learn Environment-Aware Fingerprint Generation for Sample Efficient Wireless Localization
Jun Gao, Feng Yin, Wenzhong Yan +3
Existing fingerprinting-based localization methods often require extensive data collection and struggle to generalize to new environments. In contrast to previous environment-unkno…