Hey all,

A few months ago a friend and I got started on this interesting challenge: Could one accurately predict the training time of common data science algorithms such as Random Forest, Svm or Kmeans? Our python package called “Scitime” (which you can pip or conda install) is the result of our effort to build a scalable solution that can be applied to any Scikit learn algorithms in the future. We detailed our methodology and findings in this article

We’re looking for user feedback – try it out and let us know!

I’m trying to upgrade my workstation for running Keras/Tensorflow but I’m having a hard time finding benchmarks for GPUs.

For example, how much better is a 2080 Ti vs buying multiple old 1080s.

Anyone know of a good review website that includes machine learning benchmarks?

For all the research advances in machine learning, it certainly feels like the rewards are still collected by a select group of large tech companies based in traditional tech giant countries (US, China, etc). Furthermore, a large portion of our best ML engineers and scientists are working on systems with the primary objective of manipulating targeted audiences through advertising and social media. To me, this feels like a failing of our community when the same advanced models and techniques could be making a direct impact on climate change or development schemes.

I’m interested in what the community thinks about this. Are we locked in this cycle where we are left hoping that advances in machine learning trickle down to less profitable, but maybe more crucial, problems or is there a systematic change that we can make to democratize these techniques so that we see real world impact on lives globally?

I haven’t found much reading on this topic from within our field recently, so if there are any interesting articles about this, that would be appreciated!

Disclaimer: No politics; not even a US citizen, this was just genuinely and objectively funny to try. Some of the generations are obviously of controversial nature

To be specific, I concatenated Trump’s tweets and the tweets of @existentialcoms which I have always found hilarious and witty and fine-tuned the language model on that and then manually posted some of the results to a parody twitter account. The resulting generations are hilarious and I’m honestly trying to stop laughing so that I can automate some of the generation/pruning/publishing and see how it looks without a human cherry picking the results.

Twitter Handle

I want to make a neural network for my Bachelor Thesis, which optimizes mechanical structures like in this paper:

” A deep Convolutional Neural Network for topology optimization with strong generalization ability ” ( Yiquan Zhanga · Airong Chena · Bo Penga · Xiaoyi Zhoua · Dalei Wang )

https://arxiv.org/ftp/arxiv/papers/1901/1901.07761.pdf

I’m new to generative Models and want to make a Neural Network similar to this one (Encoder Decoder CNN) :

https://imgur.com/ivwf0Lh

My structures will have an other input resolution (probably 50×100).

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What is some good literature to get into the topic?

Hi everyone! Yesterday I found this blog post which summarized the paper on the front of HN. I found this paper extremely interesting about their way to adopt ML/DL in production and the framework they are using to set up the experiments. Really curious to know your thoughts.

What’s your experience in optimizing object/medical image segmentation tasks? Did Adam or SGD work best for you?

GitHub code repository

It implements:

1. Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
2. Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR)
3. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN)

Say a ML paper has 7-8 authors, which is not uncommon these days.

Is it better to be the (i) 2nd author or (ii) the 2nd to last author? Why?

I’m asking in the context for career progression, grad school applications, grant credibility, etc.

Meta-learning for few-shot classification (N-way-K-shot) usually uses the same number of query examples for both training and testing. For example, in a 5-way-1-shot classification task over the miniImageNet dataset, during the training phase, there are 1 example per class in the support set and 15 examples per class in the query set. During the testing phase, it’s the same. But to be realistic, shouldn’t we use more query examples for evaluation? Of course I know the results will not be as good-looking as current ones. Moreover, the setting of the ways & shots during the training phase seems not rigorous, either.