Here are
18 public repositories
matching this topic...
Lanczos Network, Graph Neural Networks, Deep Graph Convolutional Networks, Deep Learning on Graph Structured Data, QM8 Quantum Chemistry Benchmark, ICLR 2019
Updated
Oct 18, 2019
Python
Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"
Updated
Jun 18, 2020
Python
This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.
Updated
Nov 3, 2019
Python
PyTorch code for ICLR 2019 paper: Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
[ICLR'19] Meta-learning with differentiable closed-form solvers
Updated
Dec 3, 2019
Python
Official PyTorch implementation of Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation (ICLR 2019)
Updated
May 29, 2019
Python
Using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions"
Updated
Jan 20, 2020
Jupyter Notebook
[ICLR'19] Complement Objective Training
Updated
Jan 14, 2019
Python
A simplified PyTorch implementation of GANsynth
Updated
Sep 24, 2019
Jupyter Notebook
Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
Updated
Jan 31, 2020
Python
The Reinforcement-Learning-Related Papers of ICLR 2019
✂️ Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs
Updated
Mar 9, 2019
Python
Code for the paper 'Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology'
Updated
Feb 25, 2019
Python
PyTorch implementation of "Variational Autoencoders with Jointly Optimized Latent Dependency Structure" [ICLR 2019]
Updated
Jul 14, 2019
Python
Single shot neural network pruning before training the model, based on connection sensitivity
Updated
Aug 7, 2019
Jupyter Notebook
ICLR 2020 and 2019 reviews
Updated
Nov 10, 2019
Python
We propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets
Updated
May 31, 2019
Jupyter Notebook
Updated
Jan 7, 2020
Jupyter Notebook
Improve this page
Add a description, image, and links to the
iclr2019
topic page so that developers can more easily learn about it.
Curate this topic
Add this topic to your repo
To associate your repository with the
iclr2019
topic, visit your repo's landing page and select "manage topics."
Learn more
You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session.
You signed out in another tab or window. Reload to refresh your session.