Comments (2)
Hello,
@mratsim Its an interesting question. I along with some of the organizers of this reproducibility challenge organized the first reproducibility workshop. The aim of starting the workshop was two fold.
-
Give credit or award (by having a workshop at NIPS/ICML/ICLR) to authors who verify or fails to verify the results of the paper. This could have interesting implications, as someone who tries to verify can come up with even better results (by using random ``tricks'' or by architectural changes) or if it fails to verify, it could be a crucial negative result (for ex. a method XYZ does not work in ABC settings).
-
At that time, I also felt that it is essential to revisit oldish baselines, as combining ideas from the paper's which are already published along with new regularizer's or tricks (or may be new theory) can be helpful. And hence, the idea behind organizing the workshop.
-
It could be notoriously hard to reproduce some of the papers. Like JΓΆrg Bornschein from MILA reproduced (https://github.com/jbornschein/draw) DRAW, and it was considered very hard to reproduce DRAW at that time. So people who attempt should get some reward, in form of workshop publication which could possibly be cited in the future.
I'm not sure about ICLR, but I'm pretty sure, someone would keep on organizing Reproducibility workshop at ICML and NIPS, and that should allow you to atleast attend both these conferences.
I've no involvement in this reproducibility challenge, so I'm speaking for myself.
Thanks for your hard work! π
from iclr_2019.
Hi @mratsim,
We have partnered with ReScience to publish selected reproducibility efforts in a journal publication. That way inspectors can have their efforts published with valid DOI. We will be announcing the integration and review process soon.
from iclr_2019.
Related Issues (20)
- [RC] Deep Frank-Wolfe For Neural Network Optimization [SyVU6s05K7]
- [RC] LEARNING ROBUST REPRESENTATIONS BY PROJECTING SUPERFICIAL STATISTICS OUT [rJEjjoR9K7] HOT 1
- [RC] Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks [rylV-2C9KQ]
- [RC] Biologically-Plausible Learning Algorithms Can Scale to Large Datasets [SygvZ209F7]
- [RC] How Powerful are Graph Neural Networks? [ryGs6iA5Km]
- [RC] Complement Objective Training [HyM7AiA5YX]
- [RC] SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY [B1VZqjAcYX] HOT 1
- [RC] Robustness May Be at Odds with Accuracy [SyxAb30cY7] HOT 3
- [RC] SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY [B1VZqjAcYX] HOT 1
- [RC] Knowledge Flow: Improve upon your teachers [BJeOioA9Y7]
- [RC] Critical Learning Periods in Deep Networks [BkeStsCcKQ]
- [RC] THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS [rJl-b3RcF7]
- Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
- [RC] ROBUSTNESS MAY BE AT ODDS WITH ACCURACY [SyxAb30cY7]
- [RC] Adaptive Convolutional ReLUs [https://openreview.net/revisions?id=SkgD4jAcYX]
- [RC] HIGH RESOLUTION AND FAST FACE COMPLETION VIA PROGRESSIVELY ATTENTIVE GANS [Hkxx3o0qFX]
- [RC] Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition [HJMHpjC9Ym]
- [RC] Unsupervised Adversarial Image Reconstruction [https://openreview.net/forum?id=BJg4Z3RqF7]
- [RC] Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks [B1l6qiR5F7]
- [RC] Variational Autoencoder with Arbitrary Conditioning [SyxtJh0qYm]
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. πππ
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google β€οΈ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from iclr_2019.