5 citations · 8 across the 7 of their papers we have counts for
7 papers
RS-Claw-Evolution: Environment-Feedback-Driven Evolution for Lightweight Remote Sensing Agents in Long-Horizon Tasks
Kai Ouyang, Dongyang Hou, Liangtian Liu +9
Large language model-driven remote sensing (RS) agents offer a promising approach to automating geospatial analysis. However, lightweight RS agents based on compact language models…
RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents
Liangtian Liu, Zeyuan Wang, Ziyu Li +8
The rise of multi-modal large language models (MLLMs) is shifting remote sensing (RS) intelligence from "see" to "action", as OpenClaw-style frameworks enable agents to autonomousl…
Breaking the Curse of Knowledge: Towards Effective Multimodal Recommendation using Knowledge Soft Integration
Kai Ouyang, Chen Tang, Zenghao Chai +4
A critical challenge in contemporary recommendation systems lies in effectively leveraging multimodal content to enhance recommendation personalization. Although various solutions…
Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
Kai Ouyang, Wenhao Zheng, Chen Tang +2
Post-click Conversion Rate (CVR) prediction task plays an essential role in industrial applications, such as recommendation and advertising. Conventional CVR methods typically suff…
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
Chen Tang, Kai Ouyang, Zenghao Chai +4
Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation i.e., the policy) for each layer, especially when using lar…
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach
Chen Tang, Haoyu Zhai, Kai Ouyang +3
Conventional model quantization methods use a fixed quantization scheme to different data samples, which ignores the inherent "recognition difficulty" differences between various s…
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance
Chen Tang, Kai Ouyang, Zhi Wang +4
The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort t…