![[P] nn_builder - a new package that removes the need for boilerplate code when building neural networks](https://b.thumbs.redditmedia.com/c3UlhLwCvKYwQ93SADcITDPyZod_ghB3dyXPpCQSwRk.jpg "[P] nn_builder - a new package that removes the need for boilerplate code when building neural networks") |
nn_builder is a new package that lets you build neural networks without the boilerplate code using PyTorch or TensorFlow 2.0 that some of you might find useful. See below for an example of how it works Let me know what you think and if you’d like to contribute https://github.com/p-christ/nn_builder submitted by /u/__data_science__ |
This paper mathematically proves that optimizing for bit-rate and distortion when compressing perceptual data such as images, video and audio is not the right thing to do. Optimizing for bit-rate and distortion leads to unnatural low-quality outputs, as perceived by humans. Instead, compression algorithms should directly optimize for perceptual quality, yet this will always come at the cost of increased rate or distortion. An important implication is that comparing compression algorithms by rate-distortion curves, as commonly done, is misleading.
Link to PDF: http://proceedings.mlr.press/v97/blau19a/blau19a.pdf
submitted by /u/YocB
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NLP has been moving so fast in the past few years that it makes it so difficult to keep up with all the details of the progress. We will be hosting a lunch and learn session to review some of these methods tomorrow (link), and are looking for questions and topics that we should cover. what do you think are some of the most important advancements in NLP in the past couple of years and why?
https://www.eventbrite.ca/e/state-of-nlp-in-2019-aisc-lunch-learn-tickets-62899715743
submitted by /u/tdls_to
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The project is mostly done, but if anyone is a subject expert or simply want to help with the project please send me a pull request or get in contact with me at d.snowatsymbolcomesherejbs.cam.ac.uk. Any help on this project would be greatly appreciated.
Its still very fresh so any ideas/feedback are welcome and certainly appreciated. See below for the industries/areas currently covered.
Link: https://github.com/firmai/industry-machine-learning
submitted by /u/OppositeMidnight
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Does anyone have news about what is happening with MICCAI-19 reviews?
The organizers sent an email about the delay of rebuttal period (and reviews) 12 days ago. Currently even the rebuttal timeline in the website (https://www.miccai2019.org/information/information-conference_timeline/) is updated to TBD (It was previously 27-30 May, then updated to 3-7 June, and now TBD).
submitted by /u/redlow0992
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The article share the method used by PixLab to port the real-time face detection runtime written in pure C of the SOD Computer Vision library to WebAssembly to achieve real-time face detection in the browser.
Link: https://sod.pixlab.io/articles/porting-c-face-detector-webassembly.html
submitted by /u/histoire_guy
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I recently read this paper on the focal loss and there seem to be some contradictions in the paper. The first sentence of the conclusion is “In this work, we identify class imbalance as the primary obstacle preventing one-stage object detectors from surpassing top-performing, two-stage methods.”
However, when they used a simple weighting mechanism to balance the background and sought classes, the results were not significantly improved from using no weighting (alpha = 0.5), and when they employ their focal loss, they actually upweight background samples (alpha = 0.25). Is it not a misinterpretation of their results to say that their improvement comes from resolving a class imbalance?
submitted by /u/depressed_hooloovoo
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By myself only. As you have seen, I am not an expert on everything.
![[R] Applying Machine Learning and Discrete Choice Modeling to understand the quality of urban landscape](https://b.thumbs.redditmedia.com/6HiUamY7irwBPmW1S6OsNQIW0QF_AzGlCEk2u8_Q4uY.jpg "[R] Applying Machine Learning and Discrete Choice Modeling to understand the quality of urban landscape") |
Wekun (wekun.ing.puc.cl) is a game that seeks to understand how people perceive public space and, thus, understand what determines the quality of these spaces.We ask people to chose between images to measure their preferences. Then, the information collected is processed with Machine Learning algorithms and discrete choice models, in order to understand the role played by different elements of the built environment and nature in the preferences of people. The methodology is not new (links below), but we have incorporated a section to register sociodemographic information aiming to find heterogeneity among observers. We use Discrete Choice Modelling as a benchmark to Machine Learning Algorithms, typically referred to as black boxes, to overcome the explainability problems involved with them. https://i.redd.it/xamzwiv0a9231.jpg Please comment on the following subjects to help us!
Some references: Rossetti, T., Lobel, H., Rocco, V., & Hurtubia, R. (2019). Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach. Landscape and urban planning, 181, 169-178. (link) Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: mapping the inequality of urban perception. PloS one, 8(7), e68400. (link) Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016, October). Deep learning the city: Quantifying urban perception at a global scale. In European conference on computer vision (pp. 196-212). Springer, Cham. (link) submitted by /u/tiramirez |
