Huan Li

Bridging the Information Gap: Semantic Densification and Hindsight Distillation for Cold-Start Prediction

Hao Duong Le, Yifei Gao, Huan Li, Lun Jiang, Chen Bai, Ke Xing, et al.

New-user cold-start is a critical bottleneck for e-commerce platforms: predicting user lifetime value (LTV) and conversion rate (CVR) for users with sparse interaction history. Two prior directions -- LLM-based semantic augmentation and learning using privileged information (LUPI…Read more

ABot-3DWorld 0: A Universal World Model to Explore Any 3D Space

Mingchao Sun, Luyang Tang, Yu Liu, Xu Yan, Zhan Li, Yunwei Zhang, et al.

We present ABot-3DWorld 0, a universal multimodal 3D world model that turns text, image, and video inputs into high-fidelity, explorable 3D worlds. At the heart of our framework is a unified Spatial Generative Primitive (SGP), a compact tuple of a high-quality panorama and a spat…Read more

A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images

Jie Liu, Yue Wang, Danping Zeng, Xiaowen Mao, Yunyun Yu, Huijun Feng, et al.

HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression

Yuxuan Yang, Feiyang Ren, Bowen Zeng, Dalin Zhang, Jinpeng Chen, Gang Chen, et al.

Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top-$p$ nucleus sampling) offer superior accuracy by dynamically fluctuating memory budgets, yet modern inference engines (e.g., vLLM) demand rigid, static memory patterns to leve…Read more

Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI

Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen

Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality tabular data for model training remains a significant obstacle. Numerous works have focused on tabular data augmentation (TDA) to enhance the original table with additional data, thereby impr…Read more

A Novel Spatio-Temporal Graph Convolutional Network with Attention Mechanism for PM2.5 Concentration Prediction

Xin Guan, Xinyue Mo, Huan Li

Accurate and high-resolution spatio-temporal prediction of PM2.5 concentrations remains a significant challenge for air pollution early warning and prevention. Advanced artificial intelligence (AI) technologies, however, offer promising solutions to this problem. A spatio-tempora…Read more