I recently read this paper which randomly wires image classification networks using classical random graph generators.
I’m interested in the subject but haven’t been able to find anything on it since. Has anyone heard of any new developments on the subject?
submitted by /u/MartialLemur
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![[R] Neural Oblivious Decision Ensembles](https://b.thumbs.redditmedia.com/2nHzRmMnGNj0zZ9I0CruoPevGE9ll5JlaXrC0sUKCwk.jpg "[R] Neural Oblivious Decision Ensembles") |
TL;DR: authors propose a DenseNet-like ensemble of decision trees, trained end-to-end by backpropagation and beats both xgboost and neural networks on heterogeneous (“tabular”) data. (IMHO) unlike all other “neural decision tree” methods this one worked out of the box for production scale problems without heavy wizardry. Differentiable decision tree (figure 1 from arxiv paper) ArXiv: https://arxiv.org/abs/1909.06312 Source code: https://github.com/Qwicen/node Abstract: Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems. In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data. In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning. With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks. We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data. submitted by /u/justheuristic |
Over the weekend I posted a Twitter thread of my experience with CTRL, which received a lot of attention. Today, I’m releasing a script + guides to set up the CTRL text-generating model on Google Compute Engine with just a few console commands: https://github.com/minimaxir/ctrl-gce
I also added a few more generation examples + usability guides.
Let me know how it works for you!
submitted by /u/minimaxir
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E.g., the new challenge from Lyft has a top prize of $12,000.
A model that outperforms their current implementation would presumably bring in a huge amount of value, so why are they and the other companies who sponsor these challenges so stingy on the payouts?
submitted by /u/mystikaldanger
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Paper link: https://arxiv.org/pdf/1909.05371.pdf
Just come across this submitted paper. Reading it gives me a different vibe since it must have been written by mathematicians rather than ML researcher. Nevertheless, I am still fresh in this area so I can’t tell if this is a good article or not. Any comments or critics?
submitted by /u/QueueTee314
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This article is written by Chintan Trivedi. Proximal Policy Optimization aka PPO was released by OpenAI in 2017. It is considered as the state-of-the-art algorithm in reinforcement learning. The USP of this article is its simplistic explanations and coding of PPO as well as the accompanying videos. The author also released the code in his github page.
submitted by /u/begooboi
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Good evening good sir
I am a newbie with ML, i found this code from fellow redditor parasdahal.
I can make it run with mnist datasets.
Have few questions, i’d really really really appreciate it if someone can help me
https://github.com/iqbaalmuhmd/CNNnumpy
submitted by /u/youshouldknowsz
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https://i.imgur.com/wr4VaUV.png
https://github.com/Santosh-Gupta/SpeedTorch
This is library I made for Pytorch, for fast transfer between pinned CPU tensors and GPU pytorch variables. The inspiration came from needed to train large number of embeddings, which don’t all fit on GPU ram at a desired embedding size, so I needed a faster CPU <-> GPU transfer method. This also allows using any optimizer for sparse training, since every embedding contained in the Pytorch embedding variable receives an update, previously only Pytorch’s SGD, Adagrad, and SparseAdam were suitable for such training.
In addition to augmenting parameter sizes, you can use to increase the speed of which data on your CPU is transferred to Pytorch Cuda variables.
Also, SpeedTorch’s GPU tensors are also overall faster then Pytorch cuda tensors, when taking into account both transferring two and from (overall 2.6x faster). For just transfering to a Pytorch Cuda, Pytorch is still faster, but significantly slower when transfering from a Pytorch Cuda variable.
I have personally used this to nearly double the embedding size of embeddings in two other projects, by holding half the parameters on CPU. The training speed is decent thanks to the fast CPU<->GPU exchange.
https://github.com/Santosh-Gupta/Research2Vec2
https://github.com/Santosh-Gupta/lit2vec2
There’s a bit of a learning curve for the very first time getting started with it, so as soon as you run into any sort of friction, feel free to ask a question on the project gitter
And I’ll answer them.
submitted by /u/BatmantoshReturns
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This tutorial is the follow up of the previous one, which introduced a recent addition to PySC2 known as raw observations and raw actions. Now we can take that knowledge and attempt to teach our bot how to play using reinforcement learning:
submitted by /u/kiarash-irandoust
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