arXiv subjects

Juyan Zhang

Publications and source records attributed to Juyan Zhang.

4 recordsLinked to original sources

arXiv2025

KeyPointDiffuser: Unsupervised 3D Keypoint Learning via Latent Diffusion Models

Understanding and representing the structure of 3D objects in an unsupervised manner remains a core challenge in computer vision and graphics. Most existing unsupervised keypoint methods are not designed for unconditional generative settings, restricting their use in modern 3D generative pipelines; our formulation explicitly bridges this gap. We present an unsupervised framework for learning spatially structured 3D keypoints from point cloud data. These keypoints serve as a compact and interpretable representation that conditions an Elucidated Diffusion Model (EDM) to reconstruct the full shape. The learned keypoints exhibit repeatable spatial structure across object instances and support smooth interpolation in keypoint space, indicating that they capture geometric variation. Our method achieves strong performance across diverse object categories, yielding a 6 percentage-point improvement in keypoint consistency compared to prior approaches.

cs.CV↗

arXiv2025

Heteroscedasticity of Denoising Score Matching with Generalised Smooth Noise

Score Matching (SM) is a powerful framework for estimating the log-density derivatives of a distribution without calculating its normalizing constants. This capability has made it a cornerstone across multiple domains, from classical sta- tistical estimation and energy-based models to modern diffusion-based generative models. In practice, these models rely almost exclusively on Denoising Score Matching (DSM) as a tractable proxy for score matching. This ubiquity naturally raises a fundamental question: Is DSM truly "score matching for free"? In this work, we demonstrate that DSM is not a perfect substitute. We prove that the denoising objective is inherently heteroscedastic, the variance of model parame- ters fluctuates unpredictably based on both noise levels and the underlying data geometry. This instability is baked into the mathematical structure of the DSM. To address this, we derive an ideal weighting function that equalizes this variance, yielding a homoscedastic generalization of DSM. Since the ideal weights are of- ten empirically inaccessible, we show that a practical approximation weighting function via Taylor expansion reduces gradient variance during training, at the cost of statistical optimality. Notably, this provides a theoretical justification for an existing heuristic weight used in Isotropic Gaussian Diffusion. We validate our theory across different perturbed distributions and for higher-order scores.

cs.LG↗

arXiv2024

A Probabilistic Model for Skill Acquisition with Switching Latent Feedback Controllers

Manipulation tasks often consist of subtasks, each representing a distinct skill. Mastering these skills is essential for robots, as it enhances their autonomy, efficiency, adaptability, and ability to work in their environment. Learning from demonstrations allows robots to rapidly acquire new skills without starting from scratch, with demonstrations typically sequencing skills to achieve tasks. Behaviour cloning approaches to learning from demonstration commonly rely on mixture density network output heads to predict robot actions. In this work, we first reinterpret the mixture density network as a library of feedback controllers (or skills) conditioned on latent states. This arises from the observation that a one-layer linear network is functionally equivalent to a classical feedback controller, with network weights corresponding to controller gains. We use this insight to derive a probabilistic graphical model that combines these elements, describing the skill acquisition process as segmentation in a latent space, where each skill policy functions as a feedback control law in this latent space. Our approach significantly improves not only task success rate, but also robustness to observation noise when trained with human demonstrations. Our physical robot experiments further show that the induced robustness improves model deployment on robots.

cs.RO↗

arXiv2018

D-PAGE: Diverse Paraphrase Generation

In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs. We propose a simple method Diverse Paraphrase Generation (D-PAGE), which extends neural machine translation (NMT) models to support the generation of diverse paraphrases with implicit rewriting patterns. Our experimental results on two real-world benchmark datasets demonstrate that our model generates at least one order of magnitude more diverse outputs than the baselines in terms of a new evaluation metric Jeffrey's Divergence. We have also conducted extensive experiments to understand various properties of our model with a focus on diversity.

cs.CL↗