lambdamore

7 ms·

1.
Learning to correct climate model biases(agupubs.onlinelibrary.wiley.com)
2 points by lambdamore 5y ago|0 comments
2.
Improving seasonal forecast using probabilistic deep learning(agupubs.onlinelibrary.wiley.com)
1 points by lambdamore 5y ago|0 comments
3.
Making cliamte model better using deep learning(agupubs.onlinelibrary.wiley.com)
3 points by lambdamore 5y ago|1 comments
4.
Diagnose climate models using deep adversarial learning(agupubs.onlinelibrary.wiley.com)
1 points by lambdamore 5y ago|0 comments
5.
Learning to correct climate projection biases(agupubs.onlinelibrary.wiley.com)
1 points by lambdamore 5y ago|0 comments
6.
Learning to correct climate projection biases using deep learning(agupubs.onlinelibrary.wiley.com)
2 points by lambdamore 5y ago|0 comments
7.
by lambdamore 5y ago
I do, and I consider jupyter to copy mathematica notebook, without seriously admitting it.
8.
by lambdamore 5y ago
If you could take a Bayesian perspective toward the super-resolution problem, things will make sense: given a low-res image, it corresponds to a distribution of corresponding high-res images. Which one is more likely? It depends on the prio
9.
by lambdamore 6y ago
Deepmind does not foster its future PhD, but, yes, they offer a better rewarding environment for those PhDs to flourish after they get the basic training.
10.
Improving seasonal forecast using probabilistic deep learning(arxiv.org)
59 points by lambdamore 6y ago|2 comments
11.
by lambdamore 6y ago
I consider many of these ideas have been well exploited by Wolfram Mathematica since 1988. The idea of computational thinking has been there since then. It's unfair not to give a historical review of these ideas in the beginning of the
12.
Deep Transfer Learning for El Nino Prediction(nature.com)
2 points by lambdamore 7y ago|0 comments
13.
by lambdamore 7y ago
I think Mathematica is kind of a killer app for lisp.
14.
by lambdamore 7y ago
We can compute the radiative transfer processes to infer the physical processes from the satellite images.
15.
by lambdamore 7y ago
I could recommend this paper: Schneider, Tapio, et al. "Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high‐resolution simulations." Geophysical Research Letters 44.24 (2017).
16.
Mathematica 12 Has Released(wolfram.com)
2 points by lambdamore 7y ago|0 comments
17.
Automatic Differentiation in Machine Learning: A Survey (2018) [pdf](jmlr.org)
83 points by lambdamore 8y ago|48 comments
18.
Shoucheng Zhang: Quantum Computing, AI and Blockchain: The Future of IT(youtube.com)
2 points by lambdamore 8y ago|0 comments
19.
Reinforcement Learning in NIPS 2018(medium.com)
2 points by lambdamore 8y ago|0 comments