├── README.md ├── classics ├── MCMC_and_VI.pdf ├── alternatives_to_backprop.pdf ├── batch_normalization.pdf ├── bayesian_backpropagation.pdf ├── deep_generative_models.pdf ├── dropout_bayesian_approximation.pdf ├── learning_disentangled_representations.pdf ├── mixture_density_networks.pdf ├── overcoming_catastrophic_forgetting.pdf ├── uncertainty_in_deep_learning.pdf ├── unsupervised_pretraining.pdf └── weight_normalization.pdf ├── information_theory ├── discovering_nets_with_low_complexity.pdf ├── dnn_black_box_information.pdf └── shannon_information_kolmogorov_complexity.pdf ├── mathematics ├── dataset_shift.pdf ├── distribution_specific_hardness_of_learning.pdf ├── dropout_rademacher_complexity.pdf ├── electron_proton_dynamics.pdf ├── empirical_risk_minimization.pdf ├── loss_surfaces_of_deep_neural_networks.pdf ├── loss_surfaces_of_multilayer_networks.pdf ├── mathematical_theory_of_deep_CNN.pdf ├── nesterov_differential_equation.pdf ├── qualitatively_characterizing_loss_surfaces.pdf ├── rademacher_complexity_for_deep_networks.pdf ├── risk_vs_uncertainty.pdf ├── sharp_minima_can_generalize.pdf ├── spectral_representations_CNN.pdf └── synthetic_gradients.pdf ├── neuroscience ├── biologically_plausible_deeplearning.pdf ├── deep_learning_and_neuroscience.pdf ├── deep_learning_segregated_dendrites.pdf ├── deep_learning_spiking_neurons.pdf ├── equilibrium_propagation.pdf ├── neural_nets_as_dynamical_systems.pdf ├── random_synaptic_feedback_backprop.pdf └── variational_learning_for_recurrent_spiking_networks.pdf └── statistical_physics └── non_equilibrium_physics.pdf /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Pierian-Data/foundations_for_deep_learning/HEAD/README.md -------------------------------------------------------------------------------- /classics/MCMC_and_VI.pdf: -------------------------------------------------------------------------------- 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