paidamoyo

adversarial_time_to_event

ICML 2018: "Adversarial Time-to-Event Modeling"

ais

Annealed Importance Sampling (AIS) for generative models.

bayesian_nonparametric

Experiment with Bayesian Non Parametc Models

calibration_uncertainty_t2e

IEEE TNNLS 2020: "Calibration and Uncertainty in Neural Time-to-Event Modeling"

counterfactual_survival_analysis

ACM CHIL 2021: "Enabling Counterfactual Survival Analysis with Balanced Representations"

DEAP_classification

Classify EEG Signals

learning_to_hash

Learning to Hash

structured_latent_ODEs

UAI 2022: "Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations"

survival_cluster_analysis

ACM CHIL 2020: "Survival Cluster Analysis"

TensorFlow-Tutorials

TensorFlow Tutorials with YouTube Videos

paidamoyo/adversarial_time_to_event

ICML 2018: "Adversarial Time-to-Event Modeling"

paidamoyo/survival_cluster_analysis

ACM CHIL 2020: "Survival Cluster Analysis"

paidamoyo/counterfactual_survival_analysis

ACM CHIL 2021: "Enabling Counterfactual Survival Analysis with Balanced Representations"

paidamoyo/DEAP_classification

Classify EEG Signals

paidamoyo/calibration_uncertainty_t2e

IEEE TNNLS 2020: "Calibration and Uncertainty in Neural Time-to-Event Modeling"

paidamoyo/ais

Annealed Importance Sampling (AIS) for generative models.

paidamoyo/bayesian_nonparametric

Experiment with Bayesian Non Parametc Models

paidamoyo/learning_to_hash

Learning to Hash

paidamoyo/tensorflow_deep_learning

Project for experimenting with tensor flow

paidamoyo/structured_latent_ODEs

UAI 2022: "Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations"

paidamoyo/AdversarialVariationalBayes

This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks".

paidamoyo/boltzmann-machines

Boltzmann Machines in TensorFlow with examples

paidamoyo/cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

paidamoyo/DCGAN-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

paidamoyo/DialogWAE

Source Code for DialogWAE: Multimodal Response Generation with Conditional Wasserstein Autoencoder (https://arxiv.org/abs/1805.12352)

paidamoyo/dqn

Implementation of standard DQN

paidamoyo/eval_gen

Evaluation code with models for the paper "On the Quantitative Analysis of Decoder-Based Generative Models"

paidamoyo/GloVe

GloVe model for distributed word representation

paidamoyo/importance-sampling

Code for experiments regarding importance sampling for training neural networks

paidamoyo/improved_wgan_training

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

paidamoyo/irgan

IRGAN SIGIR paper experimental code

paidamoyo/normalizing-flows-tutorial

Tutorial on normalizing flows.

paidamoyo/pytorch-examples

Simple examples to introduce PyTorch

paidamoyo/RankGAN

Implementation of Adversarial Ranking for Language Generation [ArxiV 1705.11001]

paidamoyo/Stochastic_Generative_Hashing

paidamoyo/SWEM

The Tensorflow code for this ACL 2018 paper: "Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms"

paidamoyo/textCNN_public

paidamoyo/Triangle-GAN

implementation for NIPS paper Triangle Generative Adversarial Networks

paidamoyo/VariationalDeepSemanticHashing

The implementation of the models and experiments of Variational Deep Semantic Hashing paper (SIGIR 2017)

paidamoyo/wgan

Tensorflow Implementation of Wasserstein GAN (and Improved version in wgan_v2)