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Posts by Collection
portfolio
Portfolio item number 2
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publications
Adaptive 3D Reconstruction via Diffusion Priors and Forward Curvature-Matching Likelihood Updates
Published in The Thirty-Ninth Annual Conference on Neural Information Processing Systems(NeurIPS), Spotlight, 2025
FCM is a training-free, likelihood-guided diffusion update that auto-tunes step sizes via forward-mode autodiff and curvature probes, enabling fast, stable 3D point-cloud reconstruction from single/multi-view inputs. OpenReview · Project page · Code
FAST-DIPS: Adjoint-Free Analytic Steps and Hard-Constrained Likelihood Correction for Diffusion-Prior Inverse Problems
Published in International Conference on Learning Representations (ICLR) 2026, 2026
FAST-DIPS is a training-free diffusion-prior inverse-problem solver that enforces a hard measurement-space feasibility constraint via an adjoint-free ADMM correction with an analytic (or forward-difference) step size, plus decoupled re-annealing. It supports linear/nonlinear forward operators without hand-coded adjoints and includes pixel, latent, and hybrid pixel→latent variants for faster, stable reconstructions. OpenReview · PDF · Code
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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