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Shuheng Li

2 accepted papers

2025

LEDiT: Your Length-Extrapolatable Diffusion Transformer without Positional Encoding

NeurIPS 2025poster

Diffusion transformers (DiTs) struggle to generate images at resolutions higher than their training resolutions. The primary obstacle is that the explicit positional encodings (PE), such as RoPE, need extrapolating to unseen positions which degrades performance when the inference resolution differs…

Cited by 0SourcecodeScholar
2024

UniMTS: Unified Pre-training for Motion Time Series

NeurIPS 2024poster

Motion time series collected from low-power, always-on mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications in healthcare, automation, IoT, and AR/XR. However, given security and privacy concerns, building…