141 citations · 209 across the 14 of their papers we have counts for
6 papers · 1 filter
Towards Interactive Reinforcement Learning with Intrinsic Feedback
Benjamin Poole, Minwoo Lee
Reinforcement learning (RL) and brain-computer interfaces (BCI) have experienced significant growth over the past decade. With rising interest in human-in-the-loop (HITL), incorpor…
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
Archit Parnami, Mayuri Deshpande, Anant Kumar Mishra +1
Recent advances in neural networks have solved common graph problems such as link prediction, node classification, node clustering, node recommendation by developing embeddings of…
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang +3
Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distributi…
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan +3
Federated learning (FL) is a distributed machine learning technology for next-generation AI systems that allows a number of workers, i.e., edge devices, collaboratively learn a sha…
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan +3
Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents F…
Demystifying Deep Neural Networks Through Interpretation: A Survey
Giang Dao, Minwoo Lee
Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn. The problem is that the sing…