iclr2019

There are 1 repository under iclr2019 topic.

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
579
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 555
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.
deep-learning iclr2019 lottery lottery-ticket-hypothesis network-pruning pruning python pytorch pytorch-implementation winning-ticket
Language:Python 325
lrjconan / LanczosNetwork
Lanczos Network, Graph Neural Networks, Deep Graph Convolutional Networks, Deep Learning on Graph Structured Data, QM8 Quantum Chemistry Benchmark, ICLR 2019
benchmark graph-convolutional-networks graph-neural-networks iclr2019 lanczos-network pytorch
Language:Python 311
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++ 118
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)
conditional-gan gan glcic iclr2019 mr-gan pix2pix pytorch srgan
Language:Python 92
ss12f32v / GANsynth-pytorch
A simplified PyTorch implementation of GANsynth
gan iclr2019 magenta pytorch-implementation synthesizer
Language:Jupyter Notebook 81
henry8527 / COT
[ICLR'19] Complement Objective Training
cifar10 complement-entropy complement-objective-training iclr2019 optimization pytorch
Language:Python 76
ewanlee / ICLR2019-RL-Papers
The Reinforcement-Learning-Related Papers of ICLR 2019
hierarchical-reinforcement-learning iclr2019 imitation-learning intrinsic-reward inverse-reinforcement-learning meta-learning model-based-rl model-free multiagent-reinforcement-learning online-learning reinforcement-learning robust-reinforcement-learning sequence-modeling transfer-learning
48
da-molchanov / variance-networks
Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
deep-learning dropout iclr2019 variance-networks
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 30
seilna / CNN-Units-in-NLP
:scissors: Repository for our ICLR 2019 paper: Discovery of Natural Language Concepts in Individual Units of CNNs
interpretable-deep-learning nlp cnn iclr2019
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
deep-learning iclr2019 neural-network prunning pytorch
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

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

tech-srl / code2seq

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