Discover and read the best of Twitter Threads about #NeuralNetworks
Most recents (3)
1. Let's talk about trinary ( #ternary ) and #IOTA . (Long rant, maybe better suited for a blog entry. Oh well, whatever.)
2. In my many discussions with many different people about #IOTA , ternary is I think the most controversial aspect of the tech. Many people consider it genuine lunacy, a sign that #IOTA is just a scam cryptocurrency.
3. And I'll be honest with you: when I first encountered ternary in #IOTA , I went "What the f*** is this sh**? Numerology?"
We are currently in the keynote. The speaker is talking about how popular Neo4j is
Keynote now telling us about all the new Neo4j features. Examples: location filter, including 3d. Auto cache reheating. I'm interested to know if auto cache reheating is working with query patterns or meant to replace them or what
How many random seeds are needed to compare #DeepRL algorithms?
Our new tutorial to address this key issue of #reproducibility in #reinforcementlearning
PDF: arxiv.org/pdf/1806.08295…
Code: github.com/flowersteam/rl…
Blog: openlab-flowers.inria.fr/t/how-many-ran…
#machinelearning #neuralnetworks
Our new tutorial to address this key issue of #reproducibility in #reinforcementlearning
PDF: arxiv.org/pdf/1806.08295…
Code: github.com/flowersteam/rl…
Blog: openlab-flowers.inria.fr/t/how-many-ran…
#machinelearning #neuralnetworks
Algo1 and Algo2 are two famous #DeepRL algorithms, here tested
on the Half-Cheetah #opengym benchmark.
Many papers in the litterature compare using 4-5 random seeds,
like on this graph which suggests that Algo1 is best.
Is this really the case?
on the Half-Cheetah #opengym benchmark.
Many papers in the litterature compare using 4-5 random seeds,
like on this graph which suggests that Algo1 is best.
Is this really the case?
However, more robust statistical tests show there are no differences.
For a very good reason: Algo1 and Algo2 are both the same @openAI baseline
implementation of DDPG, same parameters!
This is what is called a "Type I error" in statistics.
For a very good reason: Algo1 and Algo2 are both the same @openAI baseline
implementation of DDPG, same parameters!
This is what is called a "Type I error" in statistics.
