We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D
geometries, using only large-scale text-guided image diffusion models. In contrast to recent …

While modern machine learning models rely on increasingly large training datasets, data is
often limited in privacy-sensitive domains. Generative models trained with differential privacy …

Although machine learning models trained on massive data have led to breakthroughs in
several areas, their deployment in privacy-sensitive domains remains limited due to restricted …

Motivation: Deep learning models deployed for use on medical tasks can be equipped with
Out-of-Distribution Detection (OoDD) methods in order to avoid erroneous predictions. …

Few-shot classification is the task of predicting the category of an example from a set of few
labeled examples. The number of labeled examples per category is called the number of …

• Decision-support systems or clinical prediction tools based on machine learning (including
the special case of deep learning) are similar to clinical support tools developed using …

Background Current upper extremity outcome measures for persons with cervical spinal
cord injury (cSCI) lack the ability to directly collect quantitative information in home and …

Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to
applying machine learning algorithms in practice. Transfer learning is a popular strategy for …

This paper investigates the e N method for predicting the laminar-to-turbulent transition in
the context of hybrid laminar flow control. Transonic wind tunnel experiments are used to …

Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming
optimization that can take up to an hour per prompt. Amortized methods like ATT3D …