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07/28/2023
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design
Federated Learning (FL) has been an area of active research in recent ye...
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10/14/2022
Neural Routing in Meta Learning
Meta-learning often referred to as learning-to-learn is a promising noti...
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08/20/2022
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
Classical federated learning approaches yield significant performance de...
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08/06/2022
NeuCASL: From Logic Design to System Simulation of Neuromorphic Engines
With Moore's law saturating and Dennard scaling hitting its wall, tradit...
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06/28/2022
LiteCON: An All-Photonic Neuromorphic Accelerator for Energy-efficient Deep Learning (Preprint)
Deep learning is highly pervasive in today's data-intensive era. In part...
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05/02/2021
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
The traditional approach in FL tries to learn a single global model coll...
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03/04/2021
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks
This paper proposes a new paradigm for learning a set of independent log...
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06/18/2020
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Unsupervised meta-learning approaches rely on synthetic meta-tasks that ...
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06/16/2020
Differentially-private Federated Neural Architecture Search
Neural architecture search, which aims to automatically search for archi...
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08/01/2018
Network Optimization for Unified Packet and Circuit Switched Networks
Internet traffic continues to grow unabatedly at a rapid rate, driven la...
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04/05/2018