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I've been trying to replicate the results of your leaderboard, but I found a number of things confusing (based on the "medium" data in the linked colab):
1) leaderboard is based on "realworld" level,…
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Post your question here about the orienting readings: “[Preface: How to Think with Deep Learning](https://docs.google.com/document/d/1_IiBXAPDNmGHXk2ETmr85eKT9BgjOFqB72D0nRsqnEY/edit?usp=sharing)”, “[…
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Post a reading of your own that uses deep learning for social science analysis and understanding, with a focus on strategic sampling and active learning. Here, you may also choose examples that built …
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## Overview
Currently, we use a deterministic SIR model (see `sir` and `sim_sir` in [models.py](https://github.com/CodeForPhilly/chime/blob/develop/penn_chime/models.py)) to predict everything. It …
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### Metadata
- Authors: Christopher P. Burgess, Irina Higgins, +4 authors Alexander Lerchner
- Organization: DeepMind
- Publish Date: 2018.04
- Paper: https://arxiv.org/pdf/1804.03599.pdf
- 3rd-p…
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### Deep Learning Simplified Repository (Proposing new issue)
:red_circle: **Project Title** : Glioma MRI Human Brain Tumor Detection
:red_circle: **Aim** : The aim is to identify the brain tumors f…
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```ErgonomicFugitive```> What you're asking for is bayesian probability analysis, though that's always going to be highly approximate when you don't have access to the data sets themselves. Short of t…
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Hi!
I'm currently working on new project [Gelato](https://github.com/ferrine/gelato) that can be interesting for Lasagne users. It links PyMC3 and Lasagne for constructing neural networks from bayesi…
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I'm trying to train a bayesian LSTM to predict remaining useful lifetime using windows of ten samples with roughly 600 features. I previously trained a conventional LSTM in tensorflow and therefore re…
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is there a reason why the agents were implemented this way?
any paper that proves that it gives an advantage over the "normal" method (if bought/sold true -> only close position or nothing)?