tychovdo Goto Github PK

abc

SeqGAN but with more bells and whistles

asdlfkjsadlkfj

char-rnn-understanding

Understanding Character-level RNN Language Models with Diagnostic Classifiers in PyTorch.

ella

flownet2-pytorch

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

functional-layers

geo-vec

Implementation of the Geo-Vec model for embedding documents

geo-vec-model

Implementation of the Geo-Vec model for embedding documents

glow-pytorch

pytorch implementation of openai paper "Glow: Generative Flow with Invertible 1×1 Convolutions"

invertiblelanguagemodel

Stacked RNN Language Model with invertible recurrent blocks

kr

https://www.overleaf.com/6579226mjcdjb

lila

Code for "Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations"

lwjgl-topdownshooter

lwjgl_platformer

Java platformer game using the lwjgl library

memcnn

PyTorch Framework for Developing Memory Efficient Deep Invertible Networks

mml-autumn-2022

Materials for Autumn 2022 Mathematics for Machine Learning course.

movingmnist

Moving MNIST as PyTorch Dataset

mypy

Optional static typing for Python

nodejs_multiplayergame

Multiplayer game in javascript using websockets and node.js

pacmandqn

Deep Reinforcement Learning in Pac-man

real-nvp

Implementation of Real NVP in PyTorch

revgan

RevGAN implementation in PyTorch. We extend the Pix2pix and CycleGAN framework by exploring approximately invertible architectures in 2D and 3D. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep without requiring additional memory. In the paper we demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.

revgan.github.io

tychovdo.github.io

website2021

Mark's website. Edit this to add your name to the research group.