Someone made an interactive browser-based scroller game where the levels are based on music generated by a recurrent neural network model.

game: https://vibertthio.com/runn/

(I tested it on Chrome- seems to have issues with other browsers)

code: https://github.com/vibertthio/runn

blog: https://medium.com/@vibertthio/i-built-2-games-to-make-machine-learning-fun-3668ef871eae

I got a request for review for Journal of Machine Learning Research. I just finished my 3rd semester, I wrote some conference reviews which my advisor said were quite good. My advisor also said that reviewing a journal paper might take me a full week… Is it worth to do it? Can I get into trouble if the review is qualitatively not great?

I wanted to detect hand pose from on a video, on RGB images. What models perform the best?

Any links to code/ datasets that are used for hand pose detection would be great!

Thanks a lot!

Blog Post: http://gradientscience.org/robust_apps/

Paper: http://gradientscience.org/robust-apps.pdf

TL;DR: Bunch of Computer Vision applications (generation, superresolution, inpainting, etc.) with just a single robustly trained classifier, straightforwardly scales to (1K-class, 224px) ImageNet.

​

We show that a single classifier trained on a standard dataset can be leveraged for diverse computer vision applications. Using *only* an adversarially trained classifier (no generative architecture, just a standard ResNet trained with cross-entropy loss), we show that we can perform image generation, super resolution, inpainting, and interactive editing. The approach shows no instability and trivially scales to full (224×224) ImageNet. Our results suggest the robust classification framework as a viable alternative to more complex or task-specific approaches.

Let’s say for my research project I use a model in part of my implementation (e.g. To calculate semantic similarities between sentences) since it is the best suited for the particular task at hand. Then in the evaluation of my implementation and those of my competitors, can I use the same model (e.g. To calculate semantic similarities between output and input) or would this be inappropriate since it was used in my own implementation? It wouldn’t make much sense to use a different model for the evaluation since it is the “best suited” both for implementation and evaluation. What should the approach be here?

https://pytorch.org/blog/towards-reproducible-research-with-pytorch-hub/

​

“Reproducibility is an essential requirement for many fields of research including those based on machine learning techniques. However, many machine learning publications are either not reproducible or are difficult to reproduce. With the continued growth in the number of research publications, including tens of thousands of papers now hosted on arXiv and submissions to conferences at an all time high, research reproducibility is more important than ever. While many of these publications are accompanied by code as well as trained models which is helpful but still leaves a number of steps for users to figure out for themselves.

We are excited to announce the availability of PyTorch Hub, a simple API and workflow that provides the basic building blocks for improving machine learning research reproducibility. PyTorch Hub consists of a pre-trained model repository designed specifically to facilitate research reproducibility and enable new research. It also has built-in support for Colab, integration with Papers With Code and currently contains a broad set of models that include Classification and Segmentation, Generative, Transformers, and more.”

Hey guys,

I’ve written a guide to a basic implementation of machine learning to predict if a ball will be a wicket in a cricket match. In case anyone here is a Moneyball fan who wants to give it a go, I’ll post here. Details about how to get the dataset etc are in the article.

https://medium.com/auquan/a-guided-approach-to-using-machine-learning-for-cricket-wicket-prediction-5ff0e0e2313d?postPublishedType=initial

Hello everyone,
I apologize for my incorrect English, I hope you understand my problem anyway.
I state that I am new to the machine learning sector, being a university student. I’m looking for some repository or someone to explain to me step by step (in short, something already ready) how to use my .record (Tensorflow records) with any YOLO algorithm in Object Detection.
I tried almost any Git repository found on the web regarding this problem but 99% do not use Tensorflow records and those few who used them had errors in the execution of the various phases of training, detection etc …
My dataset, as I have already heard, is formed by three .record (train, validation, test), where we have for each Bounding Box each image: Xmin, Xmax, Ymin, Ymax, Width, Height and a total of 3 classes (low, medium, high).
I ask you this because otherwise I would be forced to create xml files for each image and then use the classic Pascal VOC notation for training.
I can use Google Colab without problems.
I hope I was clear, thank you in advance !!

For recurrent neural networks (RNNs) increasing the number of units allows the network to (better) model a relationship over more distant inputs in an input sequence.

However what’s the effect of increasing the number of layers in a transformer? Since the transformer looks at multiple inputs of the sequence simultaneously at each layer – it doesn’t have an analogue with RNNs.