I made a data loader based on the tf data API which works for semantic segmentation problems. It is also able to do random augmentations (brightness, contrast, saturation, random_crop, flips).
You can find it here
If you have any suggestions to improve the functionality or add more features, would love any feedback!
Hello guys,
For a fantastic NLP package it is, Gensim is not receiving the attention it deserves. May be the native tutorials aren't as easy to grasp compared to other NLP packages. So I wrote a gensim tutorial for beginners who haven't been introduced.
Thanks
Hey there!
I published the 7th article of the Deep Reinforcement Learning series, about the most promising innovation in Deep Reinforcement Learning : Curiosity Driven Learning.
THE ARTICLE: https://towardsdatascience.com/curiosity-driven-learning-made-easy-part-i-d3e5a2263359
For those who don't know what is Curiosity Driven Learning, the idea is to build a reward function that is generated by the agent itself 🤖.
It means that the agent will be a self-learner since he will be the student but also the feedback master. That's just awesome.
Let me say what you think!
![[P] 73% Faster, 60% less RAM Sparse Pairwise Euclidean/L2 Distances](http://b.thumbs.redditmedia.com/_vnNsh1hO6GLdQUmFz94ixnB8qVlhmjFDFBL2IYsToU.jpg "[P] 73% Faster, 60% less RAM Sparse Pairwise Euclidean/L2 Distances") | Just wanted to report that HyperLearn's ongoing development is great! I just updated it today, and wanted to report that HyperLearn's L2 pairwise distances on itself dist(X, X) is now 29% faster on Dense Matrices, and 73% faster on Sparse Matrices!!! [n = 10,000 | p = 1,000] when compared to Sklearn's Pairwise Distances and Euclidean Distance modules. 60% less Memory usage is seen. Also, I tested on [n = 20,000 | p = 1,000]. Sadly, Sklearn got a Memory Error, whilst HyperLearn still succeeded! https://github.com/danielhanchen/hyperlearn
I'll wrap it up into modules, but I am planning to rewrite TSNE most likely next, as it uses Nearest Neighbors on itself (dist(X,X)) Maybe you missed it, but NNMF (Non negative Matrix Factorisation) is also approx 50% faster in HyperLearn! Code also pushed. Note I'll make them into modules later. Likewise, if anyone wants to contrib, email me @ [danielhanchen@gmail.com](mailto:danielhanchen@gmail.com) or message me! [link] [comments] |
![[R] Just the error of fitting to a random convolutional network is a reward signal that can solve Montezuma's Revenge](http://a.thumbs.redditmedia.com/O5xSqPxXQan0eq4XIj_39G9lsyFLtyg3D_81hYNOIr4.jpg "[R] Just the error of fitting to a random convolutional network is a reward signal that can solve Montezuma's Revenge") | submitted by /u/downtownslim [link] [comments] |
I am a graduate student studying deep learning and computer vision. I will start my research next year and I have always assumed I would pick a sub-field in computer vision, but recently in the United States the baseball playoffs started and one of the marketing campaigns is for google assistant, which got me thinking about NLP. I have never really been interested in NLP and have maybe used Siri once on my iPhone, but the commercials that google kept exposing me to got me thinking, and I started asking Siri some more thought provoking questions, like "Who is/was the most superstitious Baseball Player?", and "Who is the most misunderstood athlete?". Obviously the answers were lack luster, but I am very intrigued why that is... Although they are subjective questions with no absolute, correct answer, I think a decent answer could be achieved, like a list of some very superstitious players and why they are considered superstitious.
At first, I thought the missing key is mainly data. There is probably no data set that maps baseball players to there superstitious tendencies, and the only way to really get this information would be to comb over thousands and thousands of hours of game color commentary, player interviews, and any other media that might lend itself to being superstitious (a nearly impossible task). This automatically made me question, can we teach an algorithm what superstitious behavior even is?
These questions have me very interested and I want to pursue them. Who is doing research in this type of field? How far have they come? Do we think we can reasonably solve problems like this or, at least, make some progress in the near future, or are we simply missing too many pieces to the puzzle?
Thanks!
Hi fellows,
I am a senior year Ph.D. student in US (CS program ranking around 50 only) working on inferring causal effects and relationships with machine learning. I only have several papers in 2nd-tier conferences like SDM, CIKM and IJCAI. I wonder if it is possible to get an internship about causal inference from companies/research labs. I am also heading to CIKM'18 next week, hope can meet some of you there.
If you are interested in my background, please pm me.
Hi, I was wondering what architectures are out there that produce images similar to the input data set. Lets suppose I was trying to predict what a 'cool' car looks like, my data set would include images of cars that I believe to be 'cool' and the network would output new cars with features from the ones from the inputs, with the hope being that these cars are also 'cool'.
I have come across Deep Convolutional Generative Averserial Networks (DCGANs) but I was wondering if there are any other architectures out there that are good for this.
Thanks for your help.
Blog post: https://deepmind.com/blog/trfl/
Github link: https://github.com/deepmind/trfl
Good to see some open-source efforts from DeepMind, although a bit late.
(I posted this just now in /r/computervision as well, hope cross-posting is not frowned upon, didn't see it in the rules, sorry otherwise!)
I haven't done a CV task before where the available dataset have been this big, or of this exceptional quality.
Every image of this dataset of seven million+ user-uploaded pictures have been painstakingly labeled manually by our "community support" team over the last 10 years, plus the addition of volunteers from the social networks where the pictures where uploaded (ten or so people has historically been required to have chosen the same label for a picture before the label was assigned).
The dataset is near perfection, with extremely few mislabeled images (except for human bias, though would have to be collectively biased since multiple people needs to miss-classify).
This dataset has six labels:
(animatemeans dick pics and titties more or less,inanimateis non-human, e.g. dildos and forests; genitals and pr0n is labeled 18+, any nipple showing or more sexual than that is labeled 16+ if not enough to earns the 18+ stamp, anything else is just labeled 12+ since we don't allow users below this age to use our services.)
The task at hand is to automatically label pictures that we legally can't show to users that are <16 years old. The laws basically boils down to this (which the manual labeling has followed):
"if a nipple and/or anything more sexual can be seen in a picture, the user needs to be at least 16 years old to see it."
My initial idea is to use a pre-trained VGG19 or ResNet50 model, lock a number of first layers and do transfer learning on whatever number of later layers show promise, and if the results are bad, experiment with a combination of AWS Rekognition and a custom solution.
Any thoughts, tips, guidelines? Appreciate any feedback!
NB: CV is not my main focus at work (though I've studied and played around with it quite a lot); I'm usually involved in time-series and NLP, and I have a much stronger comp-sci background than stats, but focusing on bridging that gap the last few years.
In this paper the authors propose 2 new multi-stage architectures for generating high resolution images and obtain very impressive results
paper review: https://youtu.be/PXWIaLE7_NU
paper ref: https://arxiv.org/abs/1710.10916
While designing new models I usually fail to identify confidently the source of the empirical gains, is it something related to the model architecture or because of a better hyper-parameter tuning. This is a common problem in the literature as mentioned in [1]
Are there less expensive techniques that one should follow to do a "fair" comparison between two models, rather than the obvious one of trying grid search over hyper param of the two models and picking the one with the best performance.
While reading Ludwig Wittgenstein's Tractatus Logico-Philosophicus, first published in English in 1922, I got the idea that machine intelligence could be created by representation. After all, W said that we picture facts to ourselves and a picture is a model of reality. This is representation. In a picture the elements of the picture are the representatives of objects. So if in thinking we use representatives of objects, perhaps a representative of intelligence would be viewed as intelligence by us.
All very philosophical, I know, but I believe this thinking leads to a different method of making software I can talk to, one that doesn't require scripting a response for anything I might say, or mapping a brain's nodes with mathematics and paying for a lot of compute time to train the system. :)
I've created representation of intelligence, and it's available here. I'd like to discuss. I'd particularly like to hear how it can be improved.
I am trying to make an custom image classifier by using two or more images as an input images. As I try to search some of documents and ref, it can do by training a whole new model. But how about using pre-trained model for the best weight and better performance? How the layer sequence should be in this case?
Date: January 27th or 28th, 2019
Location: Honolulu, Hawaii
Submission deadline: November 5th, 2018
Web site: http://aaai-rlg.mlanctot.info/
Description
Games provide an abstract and formal model of environments in which multiple agents interact: each player has a well-defined goal and rules to describe the effects of interactions among the players. The first achievements in playing these games at super-human level were attained with methods that relied on and exploited domain expertise that was designed manually (e.g. chess, checkers). In recent years, we have seen examples of general approaches that learn to play these games via self-play reinforcement learning (RL), as first demonstrated in Backgammon. While progress has been impressive, we believe we have just scratched the surface of what is capable, and much work remains to be done in order to truly understand the algorithms and learning processes within these environments.
The main objective of the workshop is to bring researchers together to discuss ideas, preliminary results, and ongoing research in the field of reinforcement in games.
We invite participants to submit papers, based on but not limited to, the following topics:
Structure and Submission Instructions
RLG is a 1 full-day workshop. It will start a 60 minute mini-tutorial covering a brief tour of the history and basics of RL in games, 2-3 invited talks by prominent contributors to the field, paper presentations, a poster session, and will close with a discussion panel.
Papers must be between 4-8 pages in the AAAI submission format, with the eighth page containing only references. Papers will be submitted electronically using Easychair. Accepted papers will not be archival, and we explicitly allow papers that are concurrently submitted to, currently under review at, or recently accepted in other conferences / venues.
Easychair link: https://easychair.org/conferences/?conf=aaai19rlg
Programme Committee
Organizers
Marc Lanctot (DeepMind)
Julien Perolat (DeepMind)
Martin Schmid (DeepMind)
Ideally something like:
The age-old question on if a product manager should be technical or not. When it comes to ML, what key concepts should a PM understand if partnering with an engineer to build a consumer facing app that is powered by ML?
