Approx_LSH: Contains implementation of ALSH-approx. Run main.py to see the results. The configuration can be changed in main.py.
Regular_NN: Contains implementation of vanilla neural network. Run main.py to see the results.
DropOut: Contains implementations of Dropout and Adaptive Dropout. Run main.py to see the results.
Activation function: ReLU; weight initialization: Kaiming initialization
Loss Function: Negative Log-Likelihood
Datasets: MNIST, NORB, Fashion-MNIST
Input and output dimension must change according to the dataset.
| Dataset | Input | Output |
|---|---|---|
| NORB | 9216 | 5 |
| MNIST | 784 | 10 |
| Fashion-MNIST | 784 | 10 |
Check out MC-approx here: https://github.com/acsl-technion/approx
Find the supplementary material in Appendix.pdf.