3 citations · 6 across the 8 of their papers we have counts for
8 papers
GLUE: Gradient-free Learning to Unify Experts
Jong-Ik Park, Shreyas Chaudhari, Srinivasa Pranav +2
In many deployed systems (multilingual ASR, cross-hospital imaging, region-specific perception), multiple pretrained specialist models coexist. Yet, new target domains often requir…
GradNetOT: Learning Optimal Transport Maps with GradNets
Shreyas Chaudhari, Srinivasa Pranav, José M. F. Moura
Monotone gradient functions play a central role in solving the Monge formulation of the optimal transport (OT) problem, which arises in modern applications ranging from fluid dynam…
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
Jong-Ik Park, Srinivasa Pranav, José M. F. Moura +1
Foundation models are now a major focus of leading technology organizations due to their ability to generalize across diverse tasks. Existing approaches for adapting foundation mod…
Peer-to-Peer Learning Dynamics of Wide Neural Networks
Shreyas Chaudhari, Srinivasa Pranav, Emile Anand +1
Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving man…
Gradient Networks
Shreyas Chaudhari, Srinivasa Pranav, José M. F. Moura
Directly parameterizing and learning gradients of functions has widespread significance, with specific applications in inverse problems, generative modeling, and optimal transport.…
Peer-to-Peer Learning + Consensus with Non-IID Data
Srinivasa Pranav, José M. F. Moura
Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a centr…
Peer-to-Peer Deep Learning for Beyond-5G IoT
Srinivasa Pranav, José M. F. Moura
We present P2PL, a practical multi-device peer-to-peer deep learning algorithm that, unlike the federated learning paradigm, does not require coordination from edge servers or the…
Learning Gradients of Convex Functions with Monotone Gradient Networks
Shreyas Chaudhari, Srinivasa Pranav, José M. F. Moura
While much effort has been devoted to deriving and analyzing effective convex formulations of signal processing problems, the gradients of convex functions also have critical appli…