4 citations · 4 across the 14 of their papers we have counts for
3 papers · 1 filter
Robust Stochastically-Descending Unrolled Networks
Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro
Deep unrolling, or unfolding, is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. However, the conv…
Learning State-Augmented Policies for Information Routing in Communication Networks
Sourajit Das, Navid NaderiAlizadeh, Alejandro Ribeiro
This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access t…
Primal Dual Continual Learning: Balancing Stability and Plasticity through Adaptive Memory Allocation
Juan Elenter, Navid NaderiAlizadeh, Tara Javidi +1
Continual learning is inherently a constrained learning problem. The goal is to learn a predictor under a no-forgetting requirement. Although several prior studies formulate it as…