5 accepted papers
In recent years, the compression of large language models (LLMs) has emerged as a key problem in facilitating LLM deployment on resource-limited devices, reducing compute costs, and mitigating the environmental footprint due to large-scale AI infrastructure. Here, we establish the foundations of LLM…
In magnetic resonance imaging (MRI) slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices corrupted by motion. While promising current SVR methods require multiple slice stacks for accurate 3D reconstruction le…
We propose a joint albedo-normal approach to non-line-of-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT). While current NLOS imaging methods reconstruct either the albedo or surface normals of the hidden scene, the two quantities provide complementary informati…
We propose a new, filtering approach for solving a large number of regularized inverse problems commonly found in computer vision. Traditionally, such problems are solved by finding the solution to the system of equations that expresses the first-order optimality conditions of the problem. This can…
We propose a rate-distortion optimized framework for estimating illumination changes (lighting variations, fade in/out effects) in a highly scalable coding system. Illumination variations are realized using multiplicative factors in the image domain and are estimated considering the coding cost of t…