1 citations · 3 across the 13 of their papers we have counts for
5 papers · 1 filter
Odin: Primitive-Level Synchronization for Distributed Point-Based Neural Rendering
Zhenxiang Ma, Zeyu He, Yuanzhen Zhou +6
Point-based neural rendering (PBNR) represents 3D scenes as explicit, trainable primitives and underpins high-quality reconstruction and emerging embodied AI and world-model pipeli…
DiTango: Cost-Effective Parallel Diffusion Generation with Selective Attention State Reuse
Yuyang Chen, Runxin Zhong, Zan Zong +3
Recent advances in AI-generated content have driven widespread adoption of Diffusion Transformers (DiTs) for high-resolution, long-duration content generation. While parallelizatio…
AdaPonderLM: Gated Pondering Language Models with Token-Wise Adaptive Depth
Shixiang Song, He Li, Zitong Wang +6
Test-time scaling via recurrent/iterative Transformers enables large language models to spend more computation at inference, but most pretrained recurrent LMs run a fixed number of…
PonderLM-3: Adaptive Token-Wise Pondering with Differentiable Masking
He Li, Feichen Song, Boyi Zeng +4
Test-time scaling has shown that allocating more additional computation at inference can improve generation quality, motivating a natural follow-up question: where should this comp…
Jano: Adaptive Diffusion Generation with Early-stage Convergence Awareness
Yuyang Chen, Linqian Zeng, Yijin ZHou +2
Diffusion models have achieved remarkable success in generative AI, yet their computational efficiency remains a significant challenge, particularly for Diffusion Transformers (DiT…