Machine Learning Made Easy with EchoBay
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EchoBay is a framework that uses Bayesian optimization to efficiently optimize hyperparameters for echo state networks (ESN), a type of recurrent neural network. It reduces the expertise required for users and provides automatic hyperparameter selection. Bayesian optimization requires fewer data samples than grid search to find optimal hyperparameters. EchoBay allows non-experts to rapidly train ESN models through an easy-to-use interface without requiring code writing.
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Machine Learning Made Easy with EchoBay
- 1. EchoBay Machine Learning Made Easy 17-31/05/2019 NGC X, San Francisco Giuseppe Franco g.franco4@studenti.unipi.it
- 2. 27 March 2019 06 March 2019 29 March 2019 11 March 2019
- 3. How can we Democratize Machine Learning? 3 Democratic Machine Learning
- 4. 1D Data Text Analysis Audio Processing Time Series 4 Time Value
- 5. An accessible Machine Learning Tool should be: Fast Easy to UseFlexible Democratic Machine Learning 5
- 6. Recurrent Neural Network x = Input A = Active Unit H = Output 6 The Input is processed by the Active Unit The output returns back as input for next time steps
- 7. Long Short-Term Memory Network Vanilla RNN Long Short Term Memory (LSTM) Networks 7
- 8. LSTM Limitations 8 Expertise Required High Amount of Data High Training Time
- 10. ● Win is Random, i.e. No Training ● W is Random, i.e. No Training ● Wout is Trained, using Least Square ● Fewer Weights ● Less Data Required ● Efficient and Fast Training Echo State Network 10
- 11. Echo State Network Hyper-Params ● How large the Reservoir? ● How to scale Win? ● How to scale W? ● How to regularize the training? ● ... The Hyper-Parameters have a fundamental role for the practical application of ESN 11
- 16. Hyper-ParamOne Hyper-Param Two Bad Good Optimal Performance Hyper-Parameters Toy Example 12 Test Every Possible Combination! Naive Approach Time Consuming Possible Bias
- 17. Our Proposed Approach: Bayesian Optimization Hyper-ParamOne Hyper-Param Two Proposed Approach Bad Good Optimal Performance 13
- 18. Hyper-ParamOne Hyper-Param Two Proposed Approach Smart Sampling Unrelated to the number of Hyper-params Fast Selection Bad Good Optimal Performance Our Proposed Approach: Bayesian Optimization 14
- 19. Bayesian Optimization reduces the required ESN-related knowledge Reducing Effort User-Side Modularity removes the necessity of code writing 15
- 20. Our Solution: EchoBay EchoBay is a framework for a smart and efficient optimization of Echo State Network. 16 User Dataset Configuration File
- 21. Our Solution: EchoBay EchoBay is a framework for a smart and efficient optimization of Echo State Network. ● Problem Definition ● Basic Hyper-Parameters Configuration ● Advanced Settings 16 User Dataset Configuration File
- 22. EchoBay Our Solution: EchoBay EchoBay is a framework for a smart and efficient optimization of Echo State Network. 17 Automatic selection of hyper-parameters User Dataset Configuration File
- 23. ● Create ESN Structures ● Train the Network ● Check performance on Validation Set ● Upgrade Hyper-Param Space Our Solution: EchoBay EchoBay is a framework for a smart and efficient optimization of Echo State Network. 17 User Dataset Configuration File Automatic selection of hyper-parameters EchoBay
- 24. User Dataset Configuration File Network Results & Optimal Configuration Structures for replicating the experiments EchoBay Automatic selection of hyper-parameters Our Solution: EchoBay EchoBay is a framework for a smart and efficient optimization of Echo State Network. 18
- 25. Results Grid Search vs Bayesian Search 19
- 26. Results Grid Search vs Bayesian Search 19 Time 44 Minutes 13 Seconds Error (NRMSE) 0.06 0.06 Performance
- 27. Conclusion Rapid and smart setup and train of ESN Easy-to-use Machine Learning framework Reduces expertise required on the user-side 20
- 28. Thanks for the attention! Luca Cerina Giuseppe Franco Marco Santambrogio Claudio Gallicchio Alessio Micheli luca.cerina@polimi.it g.franco4@studenti.unipi.it marco.santambrogio@polimi.it gallicch@di.unipi.it micheli@di.unipi.it Rapid and smart setup and train of ESN Easy-to-use Machine Learning framework Reduces expertise required on the user-side Conclusion