IgorCarron
Summary of Paris Machine Learning Meetup #7
A year in Matrix Factorization, Compressive Sensing and ML
VMX: a Kickstater for computer vision for everyone
Paris Machine Learning Meetup #6: Summary and thoughts
Summary of Paris Machine Learning Meetup #5: SARAH and Genomics
In the Paper of Catherine McGeoch and her co-author Cong Wang, they write:
"...As a case in point, our second project compares the V5 hardware chip used in our first study to a V6 chip that became operational after the study was completed. V6 is three to five times faster than V5, and can solve problems as large as n = 502...."
In other words, during the time it took to set up the algorithm and perform the study, Moore's law had been able to enable a classical approach to go five times faster. I am a big supporter of anything that does quantum computing but one should never lose sight of Moore's law.
http://nuit-blanche.blogspot.com/2013/05/randomized-thoughts...
Compressive Sensing in Light Field Photography and Hyperspectral Video Imaging
The GraphLab Workshop - Why Should You Care?
With regard to the paper you mentioned http://jrsm.rsmjournals.com/content/99/4/178.full
You need to go beyond the title of the paper. Please read the whole paper you are referencing and then please tell me how the following extract is running opposite to what I mentioned earlier:
" Opening up peer review ...The final step was, in my mind, to open up the whole process and conduct it in real time on the web in front of the eyes of anybody interested. Peer review would then be transformed from a black box into an open scientific discourse. Often I found the discourse around a study was a lot more interesting than the study itself. Now that I have left I am not sure if this system will be introduced...."
The simple fact is you understand peer review to mean how it is currently employed i.e. one time process used in the pre-publication stage (you do the review before the paper is published). That type of peer review is flawed as some bad papers still go through the process. What is proposed is having an on-going peer review so that over time, only the stronger papers stand. It really is not difficult to understand.
But once they have gone through that process and they are published it is simply very difficult to root out bad work given new data.
You might be aware of post publication peer review, yet, this model is not in use except through accidental replication exercises that sometimes uncover problems that eventually yield corrigendae or retraction. Currently it is very adhoc.
If you understand how science work beyond press releases, you'll know that the fraud and abuses will always be in the system. Post publication peer review is the only way to rooting out bad work that has been published or is in the preprint stage (I consder that if your preprint is out on arxiv or some other medium, it is in effect published).
Right now, for published papers, it is left as an exercise for journals to acknowledge they let some dubious work through. There is simply no economic incentive for a speedy process. If you read retractionwath.com often you'll notice that the current system is simply not regulating itself.
Open post publication peer review is a way to perform that function. Looking back it is formalizing the process by which people used to trust or not older work. It is also blurring the lines between preprints and published work since they are now under the same scrutiny.
I agree it is also less convenient for the press or the science press to be comfortable with this situation (post publication peer review) but Science becomes robust when it is clearly capable of rooting out bad work through processes like this one.
The peer review that is allowed by the system I mention actually enables post publication peer review. The review of papers that are already public (either in some preprint or in some published form).
Right now the only mechanism that is allowed in terms of feedback in the pre-publication peer review model, is writing to the journal for a corrigendum, or even a retraction. As witnessed in retractionwatch.com blog, few are enthusiastic about publishing corrections to papers that have gone through their inefficient pre-publication peer review process. Not only it is inefficient but it also directly yield examples such as the ones you, rightfully, point to.
Reddit, Google+, LinkedIn as Science Peer Review Systems
Predicting the Future: Randomness and Parsimony
The First Programmable Robot You've Never Heard of
What is Faster than Moore's Law and Why You Should Care
FREAK (Github) Keypoint Descriptor that outperforms SIFT, SURF, BRISK
Inpainting Algorithm on Github (OpenCV / C++)
Boy Bands & Football Fans: Algorithmic Discovery for Twitter
Videos "Machine Learning with Real-time and Streaming Applications"
GraphLab Workshop on Big Learning
Alex Smola's SML course: Recommender Systems
Implementation of the very fast FFT (MIT SFFT) is now available
Other Frighteningly Ambitious Startup Ideas
First GraphLab workshop in Bay Area (July)
http://www.quora.com/Startup-Ideas/What-are-other-Frightenin...
Ask HN: What are other "Frighteningly Ambitious Startup Ideas"?
In his recent essay, (http://paulgraham.com/ambitious.... ), PG discusses several frighteningly ambitious startup ideas. The question revolves around other ideas that PG did not discuss, Here are the one he discussed: 1. A New Search Engine 2. Replace Email 3. Replace Universities 4. Internet Drama 5. The Next Steve Jobs 6. Bring Back Moore's Law 7. Ongoing Diagnosis