Search Results for author:
Found 7 papers, 2 papers with code
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
Direct Federated Neural Architecture Search
In this paper, we present an effective approach for direct federated NAS which is hardware agnostic, computationally lightweight, and a one-stage method to search for ready-to-deploy neural network models.
Revisiting Neural Architecture Search
In this paper, instead of merely chasing slight improvements over state-of-the-art (SOTA) performance, we revisit the fundamental approach to NAS and propose a novel approach called ReNAS that can search for the complete neural network without much human effort and is a step closer towards AutoML-nirvana.
NASIB: Neural Architecture Search withIn Budget
Neural Architecture Search (NAS) represents a class of methods to generate the optimal neural network architecture and typically iterate over candidate architectures till convergence over some particular metric like validation loss.
Katib: A Distributed General AutoML Platform on Kubernetes
Automatic Machine Learning (AutoML) is a powerful mechanism to design and tune models.
Fast Neural Architecture Construction using EnvelopeNets
Fast Neural Architecture Construction (NAC) is a method to construct deep network architectures by pruning and expansion of a base network.
AMLA: an AutoML frAmework for Neural Network Design
Its key architectural features are the decoupling of the network generation from the network evaluation, support for network instrumentation, open model specification and a microservices based architecture for deployment at scale.
