iclr2019

There are 1 repository under iclr2019 topic.

tech-srl / code2seq
Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"
code2seq generating sequences from structured representations of code iclr2019
Language:Python 541
ashishpatel26 / 365-Days-Computer-Vision-Learning-Linkedin-Post
365 Days Computer Vision Learning Linkedin Post
linkedin computer-vision deep-learning iclr iclr2020 iclr2019 iclr2021 eccv eccv2020 eccv-2018 eccv2019 cvpr cvpr2020 cvpr2019 cvpr2018 jmlr iclr2018
492
rahulvigneswaran / Lottery-Ticket-Hypothesis-in-Pytorch
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.
lottery-ticket-hypothesis pytorch pytorch-implementation python iclr2019 lottery winning-ticket pruning network-pruning deep-learning
Language:Python 313
lrjconan / LanczosNetwork
Lanczos Network, Graph Neural Networks, Deep Graph Convolutional Networks, Deep Learning on Graph Structured Data, QM8 Quantum Chemistry Benchmark, ICLR 2019
graph-neural-networks graph-convolutional-networks lanczos-network iclr2019 benchmark pytorch
Language:Python 309
chihyaoma / selfmonitoring-agent
PyTorch code for ICLR 2019 paper: Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
pytorch vision-and-language navigation iclr2019
Language:C++ 117
bertinetto / r2d2
[ICLR'19] Meta-learning with differentiable closed-form solvers
iclr2019 few-shot-learning meta-learning classification deep-learning machine-learning computer-vision pytorch
Language:Python 114
soochan-lee / MR-GAN
Official PyTorch implementation of Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation (ICLR 2019)
gan conditional-gan pytorch pix2pix srgan glcic mr-gan iclr2019
Language:Python 91
ss12f32v / GANsynth-pytorch
A simplified PyTorch implementation of GANsynth
gan synthesizer pytorch-implementation magenta iclr2019
Language:Jupyter Notebook 79
henry8527 / COT
[ICLR'19] Complement Objective Training
complement-objective-training iclr2019 optimization pytorch cifar10 complement-entropy
Language:Python 74
ewanlee / ICLR2019-RL-Papers
The Reinforcement-Learning-Related Papers of ICLR 2019
iclr2019 reinforcement-learning multiagent-reinforcement-learning model-free model-based-rl hierarchical-reinforcement-learning inverse-reinforcement-learning imitation-learning sequence-modeling robust-reinforcement-learning meta-learning transfer-learning intrinsic-reward online-learning
47
da-molchanov / variance-networks
Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
deep-learning dropout variance-networks iclr2019
Language:Python 39
BorgwardtLab / Neural-Persistence
Code for the paper 'Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology'
algebraic-topology persistent-homology deep-learning neural-networks iclr2019 iclr machine-learning
Language:Python 29
seilna / CNN-Units-in-NLP
:scissors: Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs
cnn iclr2019 interpretable-deep-learning nlp
Language:Python 27
ys1998 / vae-latent-structure
PyTorch implementation of "Variational Autoencoders with Jointly Optimized Latent Dependency Structure" [ICLR 2019]
pytorch iclr2019 vae latent-variables bayesian-network
Language:Python 13
shivamsaboo17 / PySNIP
Single shot neural network pruning before training the model, based on connection sensitivity
pytorch deep-learning prunning iclr2019 neural-network
Language:Jupyter Notebook 11
Pseudomanifold / iclr-analysis
ICLR 2020 and 2019 reviews
iclr openreview iclr2019 iclr2020
Language:Python 7
MohdElgaar / DialogWAE
Implementation of https://arxiv.org/pdf/1805.12352.pdf (ICLR 2019)
deep-learning nlp dialog pytorch iclr2019 vae gan wgan vae-gan
Language:Jupyter Notebook 6
unifyid-labs / DeepGenStruct-Notebooks
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
iclr2019 generative-model image-augmentation fonts
Language:Jupyter Notebook 6

iclr2019

tech-srl / code2seq

ashishpatel26 / 365-Days-Computer-Vision-Learning-Linkedin-Post

rahulvigneswaran / Lottery-Ticket-Hypothesis-in-Pytorch

lrjconan / LanczosNetwork

chihyaoma / selfmonitoring-agent

bertinetto / r2d2

soochan-lee / MR-GAN

ss12f32v / GANsynth-pytorch

henry8527 / COT

ewanlee / ICLR2019-RL-Papers

da-molchanov / variance-networks

BorgwardtLab / Neural-Persistence

seilna / CNN-Units-in-NLP

ys1998 / vae-latent-structure

shivamsaboo17 / PySNIP

Pseudomanifold / iclr-analysis

MohdElgaar / DialogWAE

unifyid-labs / DeepGenStruct-Notebooks