├── .gitignore ├── LICENSE ├── README.md ├── ans ├── README.md ├── ans.py ├── rans_benchmark.ipynb └── uabs_benchmark.ipynb ├── bitsback ├── ans.ipynb ├── ans.py ├── bitsback.ipynb ├── bitsback.py ├── vae.ipynb └── vae.py ├── cramming ├── scratch.ipynb └── src │ └── dataloaders.py ├── data ├── binarized_mnist │ └── download.sh ├── cifar10 │ └── download.sh └── svhn │ └── download.sh ├── hmc ├── canonical_ensemble.ipynb ├── example4-leapfrog.ipynb ├── figures.ipynb └── hmc.ipynb ├── llm_fixed_point ├── 100_facts.txt ├── README.md ├── blog_gpt4o_summary.txt ├── data │ ├── blog_all.jsonl │ └── blog_trim.jsonl └── llm.ipynb ├── notebooks ├── label_refinery │ ├── __init__.py │ ├── imagenet_utils.py │ ├── label_refinery-cifar10.ipynb │ ├── label_refinery.ipynb │ ├── output-1535206418 │ │ ├── allresults.csv │ │ ├── label_refinery-use_pre0-run_num1.nbconvert.ipynb │ │ ├── label_refinery-use_pre0-run_num2.nbconvert.ipynb │ │ ├── label_refinery-use_pre0-run_num3.nbconvert.ipynb │ │ ├── label_refinery-use_pre0-run_num4.nbconvert.ipynb │ │ ├── label_refinery-use_pre0-run_num5.nbconvert.ipynb │ │ ├── label_refinery-use_pre1-run_num1.nbconvert.ipynb │ │ ├── label_refinery-use_pre1-run_num2.nbconvert.ipynb │ │ ├── label_refinery-use_pre1-run_num3.nbconvert.ipynb │ │ ├── label_refinery-use_pre1-run_num4.nbconvert.ipynb │ │ ├── label_refinery-use_pre1-run_num5.nbconvert.ipynb │ │ ├── results_preproc_0_run_1_history_iter0.csv │ │ ├── results_preproc_0_run_1_history_iter1.csv │ │ ├── results_preproc_0_run_1_history_iter2.csv │ │ ├── results_preproc_0_run_1_vars.txt │ │ ├── results_preproc_0_run_2_history_iter0.csv │ │ ├── results_preproc_0_run_2_history_iter1.csv │ │ ├── results_preproc_0_run_2_history_iter2.csv │ │ ├── results_preproc_0_run_2_vars.txt │ │ ├── results_preproc_0_run_3_history_iter0.csv │ │ ├── results_preproc_0_run_3_history_iter1.csv │ │ ├── results_preproc_0_run_3_history_iter2.csv │ │ ├── results_preproc_0_run_3_vars.txt │ │ ├── results_preproc_0_run_4_history_iter0.csv │ │ ├── results_preproc_0_run_4_history_iter1.csv │ │ ├── results_preproc_0_run_4_history_iter2.csv │ │ ├── results_preproc_0_run_4_vars.txt │ │ ├── results_preproc_0_run_5_history_iter0.csv │ │ ├── results_preproc_0_run_5_history_iter1.csv │ │ ├── results_preproc_0_run_5_history_iter2.csv │ │ ├── results_preproc_0_run_5_vars.txt │ │ ├── results_preproc_1_run_1_history_iter0.csv │ │ ├── results_preproc_1_run_1_history_iter1.csv │ │ ├── results_preproc_1_run_1_history_iter2.csv │ │ ├── results_preproc_1_run_1_vars.txt │ │ ├── results_preproc_1_run_2_history_iter0.csv │ │ ├── results_preproc_1_run_2_history_iter1.csv │ │ ├── results_preproc_1_run_2_history_iter2.csv │ │ ├── results_preproc_1_run_2_vars.txt │ │ ├── results_preproc_1_run_3_history_iter0.csv │ │ ├── results_preproc_1_run_3_history_iter1.csv │ │ ├── results_preproc_1_run_3_history_iter2.csv │ │ ├── results_preproc_1_run_3_vars.txt │ │ ├── results_preproc_1_run_4_history_iter0.csv │ │ ├── results_preproc_1_run_4_history_iter1.csv │ │ ├── results_preproc_1_run_4_history_iter2.csv │ │ ├── results_preproc_1_run_4_vars.txt │ │ ├── results_preproc_1_run_5_history_iter0.csv │ │ ├── results_preproc_1_run_5_history_iter1.csv │ │ ├── results_preproc_1_run_5_history_iter2.csv │ │ └── results_preproc_1_run_5_vars.txt │ ├── output-1535300939-label_refinery-cifar10 │ │ ├── allresults.csv │ │ ├── label_refinery-cifar10-use_pre0-run_num1.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre0-run_num2.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre0-run_num3.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre0-run_num4.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre0-run_num5.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre1-run_num1.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre1-run_num2.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre1-run_num3.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre1-run_num4.nbconvert.ipynb │ │ ├── label_refinery-cifar10-use_pre1-run_num5.nbconvert.ipynb │ │ ├── results_cifar10_preproc_0_run_1_history_iter0.csv │ │ ├── results_cifar10_preproc_0_run_1_history_iter1.csv │ │ ├── results_cifar10_preproc_0_run_1_history_iter2.csv │ │ ├── results_cifar10_preproc_0_run_1_vars.txt │ │ ├── results_cifar10_preproc_0_run_2_history_iter0.csv │ │ ├── results_cifar10_preproc_0_run_2_history_iter1.csv │ │ ├── results_cifar10_preproc_0_run_2_history_iter2.csv │ │ ├── results_cifar10_preproc_0_run_2_vars.txt │ │ ├── results_cifar10_preproc_0_run_3_history_iter0.csv │ │ ├── results_cifar10_preproc_0_run_3_history_iter1.csv │ │ ├── results_cifar10_preproc_0_run_3_history_iter2.csv │ │ ├── results_cifar10_preproc_0_run_3_vars.txt │ │ ├── results_cifar10_preproc_0_run_4_history_iter0.csv │ │ ├── results_cifar10_preproc_0_run_4_history_iter1.csv │ │ ├── results_cifar10_preproc_0_run_4_history_iter2.csv │ │ ├── results_cifar10_preproc_0_run_4_vars.txt │ │ ├── results_cifar10_preproc_0_run_5_history_iter0.csv │ │ ├── results_cifar10_preproc_0_run_5_history_iter1.csv │ │ ├── results_cifar10_preproc_0_run_5_history_iter2.csv │ │ ├── results_cifar10_preproc_0_run_5_vars.txt │ │ ├── results_cifar10_preproc_1_run_1_history_iter0.csv │ │ ├── results_cifar10_preproc_1_run_1_history_iter1.csv │ │ ├── results_cifar10_preproc_1_run_1_history_iter2.csv │ │ ├── results_cifar10_preproc_1_run_1_vars.txt │ │ ├── results_cifar10_preproc_1_run_2_history_iter0.csv │ │ ├── results_cifar10_preproc_1_run_2_history_iter1.csv │ │ ├── results_cifar10_preproc_1_run_2_history_iter2.csv │ │ ├── results_cifar10_preproc_1_run_2_vars.txt │ │ ├── results_cifar10_preproc_1_run_3_history_iter0.csv │ │ ├── results_cifar10_preproc_1_run_3_history_iter1.csv │ │ ├── results_cifar10_preproc_1_run_3_history_iter2.csv │ │ ├── results_cifar10_preproc_1_run_3_vars.txt │ │ ├── results_cifar10_preproc_1_run_4_history_iter0.csv │ │ ├── results_cifar10_preproc_1_run_4_history_iter1.csv │ │ ├── results_cifar10_preproc_1_run_4_history_iter2.csv │ │ ├── results_cifar10_preproc_1_run_4_vars.txt │ │ ├── results_cifar10_preproc_1_run_5_history_iter0.csv │ │ ├── results_cifar10_preproc_1_run_5_history_iter1.csv │ │ ├── results_cifar10_preproc_1_run_5_history_iter2.csv │ │ └── results_cifar10_preproc_1_run_5_vars.txt │ ├── resnet.py │ └── run.sh ├── masked_autoencoders │ └── made-mnist.ipynb ├── pixel_cnn │ ├── images │ │ ├── 2019-03-07-spread.png │ │ ├── 2019-03-25-spread.png │ │ ├── 2019-04-28-spread1.png │ │ ├── 2019-04-28-spread2.png │ │ ├── 2019-04-28-spread3-10comps.png │ │ ├── 2019-05-28-image1.png │ │ ├── 2019-05-28-image2.png │ │ ├── test_loss_img1.png │ │ └── test_loss_img2.png │ ├── notes.md │ ├── pixelcnn-PixelConv2D_test.ipynb │ ├── pixelcnn-test_loss-mixture.ipynb │ ├── pixelcnn-test_loss-weights.ipynb │ ├── pixelcnn-test_loss.ipynb │ ├── pixelcnn-test_loss_pixelconv2d-image.ipynb │ ├── pixelcnn-test_loss_pixelconv2d-multi-image.ipynb │ ├── pixelcnn-test_loss_pixelconv2d.ipynb │ ├── pixelcnn-test_m_s_setup.ipynb │ ├── pixelcnn.ipynb │ ├── pixelcnn.ipynb.old.ipynb │ └── pixelcnn_helpers.py ├── universal_resnet │ └── Universal Resnet.ipynb ├── vae-importance_sampling │ ├── DAG_example.ipynb │ ├── __init__.py │ ├── mlio.py │ ├── resnet.py │ ├── vae-cifar10-importance-sampling-px_z.ipynb │ ├── vae-cifar10-importance-sampling.ipynb │ ├── vae-mnist-importance-sampling.ipynb │ └── weightnorm.py ├── vae-inverse_autoregressive_flows │ ├── made.py │ ├── run.sh │ ├── vae-iaf-cifar10.ipynb │ └── vae-iaf-mnist.ipynb ├── vae-resnet │ ├── resnet.py │ ├── run.sh │ ├── vae-cifar10-depth0.nbconvert.ipynb │ ├── vae-cifar10-depth1.nbconvert.ipynb │ ├── vae-cifar10-depth2.nbconvert.ipynb │ ├── vae-cifar10-depth3.nbconvert.ipynb │ └── vae-cifar10.ipynb ├── vae-semi_supervised_learning │ ├── cnn-mnist.ipynb │ ├── cnn.ipynb │ ├── cnn_mnist_results.csv │ ├── cnn_results.csv │ ├── inception.ipynb │ ├── inception_results.csv │ ├── pca_mnist_results.csv │ ├── pca_model-mnist.ipynb │ ├── pca_model.ipynb │ ├── pca_results.csv │ ├── vae-m1-fit-mnist.ipynb │ ├── vae-m1-fit.ipynb │ ├── vae-m1-predict-mnist.ipynb │ ├── vae-m1-predict.ipynb │ ├── vae-m2-fit-mnist.ipynb │ ├── vae-m2-fit.ipynb │ ├── vae-m2-mnist_results.csv │ ├── vae-m2_results.csv │ ├── vae_m1_mnist_results.csv │ └── vae_m1_results.csv ├── variational-autoencoder.ipynb ├── variational_autoencoder-svhn │ ├── model_fit.ipynb │ ├── model_predict.ipynb │ └── process_svhn.ipynb └── varitional-autoencoder-basic.ipynb ├── optimization ├── chats │ └── Positive Definite Matrix Explained.pdf ├── index.html ├── notes.md └── prompt.md ├── realnvp ├── batchnorm_dev.ipynb ├── pytorch-realnvp-2dnormal.ipynb ├── pytorch-realnvp-celeba.ipynb ├── pytorch-realnvp-cifar10.ipynb └── pytorch-realnvp-mnist.ipynb ├── stochastic_langevin ├── normal_mixture.ipynb ├── requirements.txt └── stochastic_volatility.ipynb └── unfamiliar_examples └── data └── README.md /.gitignore: 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