89 citations · 388 across the 81 of their papers we have counts for
8 papers · 1 filter
Lifelong Reinforcement Learning with Modulating Masks
Eseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly +2
Lifelong learning aims to create AI systems that continuously and incrementally learn during a lifetime, similar to biological learning. Attempts so far have met problems, includin…
Sliced Optimal Partial Transport
Yikun Bai, Berhnard Schmitzer, Mathew Thorpe +1
Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal total amount of mas…
Is Multi-Task Learning an Upper Bound for Continual Learning?
Zihao Wu, Huy Tran, Hamed Pirsiavash +1
Continual and multi-task learning are common machine learning approaches to learning from multiple tasks. The existing works in the literature often assume multi-task learning as a…
Learning to Solve Optimization Problems with Hard Linear Constraints
Meiyi Li, Soheil Kolouri, Javad Mohammadi
Constrained optimization problems appear in a wide variety of challenging real-world problems, where constraints often capture the physics of the underlying system. Classic methods…
Wasserstein Task Embedding for Measuring Task Similarities
Xinran Liu, Yikun Bai, Yuzhe Lu +2
Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most curr…
PRANC: Pseudo RAndom Networks for Compacting deep models
Parsa Nooralinejad, Ali Abbasi, Soroush Abbasi Koohpayegani +4
We demonstrate that a deep model can be reparametrized as a linear combination of several randomly initialized and frozen deep models in the weight space. During training, we seek…