1 citations · 1 across the 2 of their papers we have counts for
10 papers
ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction
Jianan Nie, Peiyao Xiao, Kaiyi Ji +1
Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal structures exhibit infinite periodic a…
Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs
Hao Ban, Kaiyi Ji
Large language models are often adapted using parameter-efficient techniques such as Low-Rank Adaptation (LoRA), formulated as , where is the pre-trained para…
DeepMTL2R: A Library for Deep Multi-task Learning to Rank
Chaosheng Dong, Peiyao Xiao, Yijia Wang +1
This paper presents DeepMTL2R, an open-source deep learning framework for Multi-task Learning to Rank (MTL2R), where multiple relevance criteria must be optimized simultaneously. D…
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Chen Wang, Kaiyi Ji, Junyi Geng +16
Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from gene…
LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control
Peiyao Xiao, Chaosheng Dong, Shaofeng Zou +1
Multi-task learning (MTL) has been widely adopted for its ability to simultaneously learn multiple tasks. While existing gradient manipulation methods often yield more balanced sol…
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
Hao Ban, Gokul Ram Subramani, Kaiyi Ji
Multi-task learning (MTL) enables a joint model to capture commonalities across multiple tasks, reducing computation costs and improving data efficiency. However, a major challenge…