ERD

MobiVQA: Efficient On-Device Visual Question Answering

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Di!erentiable Neural Network Pruning to Enable Smart Applicationson Microcontrollers

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Federated Self-supervised Speech Representations: Are We There Yet?

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Deep Learning on Microcontrollers: A Studyon Deployment Costs and Challenges

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Protea: Client profiling within federated systems using flower

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

FedVal: Different good or different bad in federated learning

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

A first look into the carbon footprint of federated learning

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Deep learning on microcontrollers

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

μNAS: Constrained Neural Architecture Search for Microcontrollers

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

End-to-End Speech Recognition from Federated Acoustic Models

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Federated Self-supervised Learning for Video Understanding

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Can Fair Federated Learning reduce the need for Personalisation?

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Periodic Reporting for period 2 - REDIAL (Re-thinking Efficiency in Deep Learning under Accelerators and commodity and p...

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Secure aggregation for federated learning in flower

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

PROSPECT PRUNING: FINDING TRAINABLE WEIGHTSAT INITIALIZATION USING META-GRADIENTS

Project: REDIAL

Updated at: 27-04-2024

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Project acronym REDIAL

Resources

Selected filters:

MobiVQA: Efficient On-Device Visual Question Answering

Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers

Di!erentiable Neural Network Pruning to Enable Smart Applicationson Microcontrollers

Federated Self-supervised Speech Representations: Are We There Yet?

Deep Learning on Microcontrollers: A Studyon Deployment Costs and Challenges

Protea: Client profiling within federated systems using flower

FedVal: Different good or different bad in federated learning

Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution

A first look into the carbon footprint of federated learning

ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

Deep learning on microcontrollers

μNAS: Constrained Neural Architecture Search for Microcontrollers

End-to-End Speech Recognition from Federated Acoustic Models

Federated Self-supervised Learning for Video Understanding

Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates

Can Fair Federated Learning reduce the need for Personalisation?

Periodic Reporting for period 2 - REDIAL (Re-thinking Efficiency in Deep Learning under Accelerators and commodity and p...

Secure aggregation for federated learning in flower

PROSPECT PRUNING: FINDING TRAINABLE WEIGHTSAT INITIALIZATION USING META-GRADIENTS