2 citations · 3 across the 5 of their papers we have counts for
6 papers
Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networks
Yash Garg, Nebiyou Yismaw, Rakib Hyder +2
Multi-task and multi-domain learning methods seek to learn multiple tasks/domains, jointly or one after another, using a single unified network. The primary challenge and opportuni…
Compressive Sensing with Tensorized Autoencoder
Rakib Hyder, M. Salman Asif
Deep networks can be trained to map images into a low-dimensional latent space. In many cases, different images in a collection are articulated versions of one another; for example…
Incremental Task Learning with Incremental Rank Updates
Rakib Hyder, Ken Shao, Boyu Hou +3
Incremental Task learning (ITL) is a category of continual learning that seeks to train a single network for multiple tasks (one after another), where training data for each task i…
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models
Viraj Shah, Rakib Hyder, M. Salman Asif +1
The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled…
Solving Phase Retrieval with a Learned Reference
Rakib Hyder, Zikui Cai, M. Salman Asif
Fourier phase retrieval is a classical problem that deals with the recovery of an image from the amplitude measurements of its Fourier coefficients. Conventional methods solve this…
Learning Illumination Patterns for Coded Diffraction Phase Retrieval
Zikui Cai, Rakib Hyder, M. Salman Asif
Signal recovery from nonlinear measurements involves solving an iterative optimization problem. In this paper, we present a framework to optimize the sensing parameters to improve…