3 citations · 3 across the 7 of their papers we have counts for
7 papers
sketch-plot: Progressive Editing for Text-to-Image Academic Figures
Yinghao Tang, Yupeng Xie, Yingchaojie Feng +3
Text to image (T2I) models such as gpt-image-2 can now generate publication grade academic figures from a short prompt, but the output is a flat raster: a user who wants to change…
Demonstrating chart-plot: Closing the Last Mile of Academic Chart Generation
Yinghao Tang, Yupeng Xie, Yingchaojie Feng +3
Large language models can translate a researcher's intent into runnable matplotlib code, yet the resulting chart rarely lands in a paper without multiple rounds of manual revision.…
Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration
Yinghao Tang, Yupeng Xie, Yingchaojie Feng +2
Interactive articles help readers engage with complex ideas through exploration, yet creating them remains costly, requiring both domain expertise and web development skills. Recen…
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation
Yinghao Tang, Xueding Liu, Boyuan Zhang +13
Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information. While recent text-to-image (T2I) mod…
Scorpio: Serving Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference
Yinghao Tang, Tingfeng Lan, Bo Pan +3
Large Language Model (LLM) serving increasingly underpins online Web services such as conversational agents, Web search, and programming assistants, where requests carry heterogene…
mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
Zhengmao Ye, Dengchun Li, Zetao Hu +8
Transformer-based, pre-trained large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly in the emerging {\em pretrain-then-finetu…
DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud
Qinlong Wang, Tingfeng Lan, Yinghao Tang +8
Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model per…