12 citations · 31 across the 9 of their papers we have counts for
10 papers
Continuous Optimization for p-adic Models
Julian Salazar, Dimitri Kanevsky, Matt Harvey +2
We present the first method for native, continuous gradient descent for machine learning models with -adic parameters. Existing native optimizers are discrete, mostly combinator…
PhaseCoder: Microphone Geometry-Agnostic Spatial Audio Understanding for Multimodal LLMs
Artem Dementyev, Wazeer Zulfikar, Sinan Hersek +3
Current multimodal LLMs process audio as a mono stream, ignoring the rich spatial information essential for embodied AI. Existing spatial audio models, conversely, are constrained…
Solving Image PDEs with a Shallow Network
Pascal Tom Getreuer, Peyman Milanfar, Xiyang Luo
Partial differential equations (PDEs) are typically used as models of physical processes but are also of great interest in PDE-based image processing. However, when it comes to the…
Image Stylization: From Predefined to Personalized
Ignacio Garcia-Dorado, Pascal Getreuer, Bartlomiej Wronski +1
We present a framework for interactive design of new image stylizations using a wide range of predefined filter blocks. Both novel and off-the-shelf image filtering and rendering t…
Local Kernels that Approximate Bayesian Regularization and Proximal Operators
Frank Ong, Peyman Milanfar, Pascal Getreuer
In this work, we broadly connect kernel-based filtering (e.g. approaches such as the bilateral filters and nonlocal means, but also many more) with general variational formulations…
Fast, Trainable, Multiscale Denoising
Sungjoon Choi, John Isidoro, Pascal Getreuer +1
Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows re…