polceanum

neural.ca

adl2strips

ADL to STRIPS converter from Joerg Hoffmann

ANML

A Neuromodulated Meta-Learning algorithm

brain-tokyo-workshop

🧠🗼

c-swm

Code for ICLR 2020 submission "Contrastive Learning of Structured World Models"

DeepFovea

Neural Reconstruction for Foveated Rendering and Video Compression using Learned Statistics of Natural Videos

difftaichi

10 differentiable physical simulators built with Taichi differentiable programming (DiffTaichi, ICLR 2020)

dmt.test

This is just a test

downward

The Fast Downward domain-independent classical planning system

dreamer

Dream to Control: Learning Behaviors by Latent Imagination

EchoStateNetworks

Materials related to the Medium article "Predicting Stock Prices with Echo State Networks".

fnn

Embed strange attractors using a regularizer for autoencoders

forbiditerative

ForbidIterative planners for top-k, top-quality, and diverse planning problems

GANLatentDiscovery

The authors official implementation of Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

gmm-torch

Gaussian mixture models in PyTorch.

gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

Hands-On-Meta-Learning-With-Python

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

hopfield-layers

Hopfield Networks is All You Need

liquid_time_constant_networks

Code Repository for Liquid Time-Constant Networks (LTCs)

locating-objects-without-bboxes

PyTorch code for "Locating objects without bounding boxes" - Loss function and trained models

MMD-Variational-Autoencoder-Pytorch-InfoVAE

Implementation of the MMD VAE paper (InfoVAE: Information Maximizing Variational Autoencoders) in pytorch

mydmt

just a test

narrative.generation

Code for AAAI'21 paper "Narrative Plan Generation with Self-Supervised Learning"

neural-tangents

Fast and Easy Infinite Neural Networks in Python

polceanum.github.io

Personal website

Regularized_autoencoders-RAE-

reservoir

reservoir-memory-machines

rigl

End-to-end training of sparse deep neural networks with little-to-no performance loss.

Sentence-VAE

PyTorch Re-Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349