1 code implementation • ICML 2020 • Debabrata Mahapatra, Vaibhav Rajan
However, they cannot be used to find exact Pareto optimal solutions satisfying user-specified preferences with respect to task-specific losses, that is not only a common requirement in applications but also a useful way to explore the infinite set of Pareto optimal solutions.
no code implementations • 7 Jul 2022 • Debabrata Mahapatra, Chaosheng Dong, Yetian Chen, Deqiang Meng, Michinari Momma
Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e. g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue.
no code implementations • 2 Aug 2021 • Debabrata Mahapatra, Vaibhav Rajan
These shortcomings lead to modeling limitations and computational inefficiency in multi-task learning (MTL) and multi-criteria decision-making (MCDM) methods that utilize CS for their underlying non-convex multi-objective optimization (MOO).
no code implementations • 20 May 2017 • Debabrata Mahapatra, Subhadip Mukherjee, Chandra Sekhar Seelamantula
We address the problem of reconstructing sparse signals from noisy and compressive measurements using a feed-forward deep neural network (DNN) with an architecture motivated by the iterative shrinkage-thresholding algorithm (ISTA).