55 citations · 90 across the 4 of their papers we have counts for
5 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…
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation
Md Kaykobad Reza, Ashley Prater-Bennette, M. Salman Asif
Multimodal learning seeks to utilize data from multiple sources to improve the overall performance of downstream tasks. It is desirable for redundancies in the data to make multimo…
MMSFormer: Multimodal Transformer for Material and Semantic Segmentation
Md Kaykobad Reza, Ashley Prater-Bennette, M. Salman Asif
Leveraging information across diverse modalities is known to enhance performance on multimodal segmentation tasks. However, effectively fusing information from different modalities…
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…
L1-norm Tucker Tensor Decomposition
Dimitris G. Chachlakis, Ashley Prater-Bennette, Panos P. Markopoulos
Tucker decomposition is a common method for the analysis of multi-way/tensor data. Standard Tucker has been shown to be sensitive against heavy corruptions, due to its L2-norm-base…