25 citations · 50 across the 6 of their papers we have counts for
9 papers
MetaDIP: Accelerating Deep Image Prior with Meta Learning
Kevin Zhang, Mingyang Xie, Maharshi Gor +3
Deep image prior (DIP) is a recently proposed technique for solving imaging inverse problems by fitting the reconstructed images to the output of an untrained convolutional neural…
Memory-efficient Learning for High-Dimensional MRI Reconstruction
Ke Wang, Michael Kellman, Christopher M. Sandino +5
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can…
Playing with Food: Learning Food Item Representations through Interactive Exploration
Amrita Sawhney, Steven Lee, Kevin Zhang +2
A key challenge in robotic food manipulation is modeling the material properties of diverse and deformable food items. We propose using a multimodal sensory approach to interact an…
A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy
Kevin Zhang, Mohit Sharma, Jacky Liang +1
We designed a modular robotic control stack that provides a customizable and accessible interface to the Franka Emika Panda Research robot. This framework abstracts high-level robo…
Memory-efficient Learning for Large-scale Computational Imaging
Michael Kellman, Kevin Zhang, Jon Tamir +3
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classic…
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop
Michael Kellman, Jon Tamir, Emrah Boston +2
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems. Recently, critica…