Deadline: March 1, 23.59.
Save notebooks into task3/SurnameTask3.ipynb
IMPORTANT: the code must not be written in Torch/Tensorflow. For deep learning use Jax.
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[reporter: Timofey Chernikov] Implement 1-st order DARTS for CIFAR-10 dataset. Compare with random structure selection. Model: 3-layer network. Structure: binary mask for each parameter. Plots: model peformance, mask. NOTE: binary mask won't work with unconstrained parameter optimization, think how to overcome this limitation.
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[reporter: Dmitry Protasov] Implement ENAS for CIFAR-10 dataset. Compare with random structure selection. Model: 3-layer network. Structure: binary mask for each parameter. Plots: model peformance, mask.
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[reporter: TODO] Implement naive-RMAD (without information loss restoring) for 2d problem (the loss surface must be nontrivial). Plot the forward trajectory and restored trajectory for different number of epoch and different momentum (mu = 0, 0.5, 0.9, 0.99).
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[reporter: Parviz Karimov] Consider Bayesian linear regression. Restore covariance matrix using Bayesian optimization methods. The covariance matrix must be full-ranked. Plot the dependence of the restoration performance from number of iterations for different data dimmensions.
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[reporter: Marat Khusainov] Consider Bayesian linear regression. Restore covariance matrix using HOAG method. Consider full-ranked and 1-ranked covariance matrix. Plot the dependence of the restoration performance from number of iterations for different data dimmensions.
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[reporter: TODO] Implement greedy hyperparameter optimization for logistic regression on FASHION-MNIST. Compare two types of hyperparameters:
- Binary mask for each parameter
- l2-coefficients for each parameter.
NOTE: binary mask won't work with unconstrained parameter optimization, think how to overcome this limitation.
Compare results and hyperparameters.
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[reporter: Boeva Galina] Implement a genetic algorithm for model structure selection for CIFAR-10 dataset. Compare with random structure selection. Model: 3-layer network. Structure: binary mask for each parameter. Plots: model peformance, mask.
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[reporter: TODO] Implement a ENAS-based symbolic regression.
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[reporter: Kseniia Petrushina] Consider Bayesian linear regression. Restore covariance matrix using Bayesian optimization methods. The covariance rank matrix must vary from 1 to full-rank. Plot the dependence of the restoration performance from number of iterations for different ranks.