Bayesian Neural Network in PyTorch
-
Updated
May 3, 2024 - Python
Bayesian Neural Network in PyTorch
This repository is associated with the paper "Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling", accepted at EACL 2023.
Machine learning Algorithms for the Prediction of Successful Aging in Older Adults
PyTorch implementation of 'Concrete Dropout'
the implementation of a multilayer perceptron
[PAKDD 2022] Auxiliary Local Variables for Improving Regularization/Prior Approach in Continual Learning
A Deep Learning framework for CNNs and LSTMs from scratch, using NumPy.
Implementation of a Fully Connected Neural Network, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) from Scratch, using NumPy.
A web app where user can draw Bengali digit and the AI model can detect handwritten digit and predict the digit.
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Artificial Intelligence Learning Notes.
Manual pure scratch code of Convolution-Pooling-Dropout
[ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal, Shiwei Liu, Zhangyang Wang
Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".
Code for Checkerboard Dropout paper
Neural Network aided diagnosis of Schizophrenia via patient-centered text Data
Concrete Dropout implementation for Tensorflow 2.0 and PyTorch
📄 Official implementation regarding the paper "Fine-Tuning Dropout Regularization in Energy-Based Deep Learning".
Add a description, image, and links to the dropout topic page so that developers can more easily learn about it.
To associate your repository with the dropout topic, visit your repo's landing page and select "manage topics."