I think my original post was removed because I didn’t tag it.

I have a project in mind. I want to build an image classifier with novel classes. For example, lets say I want to classify images of different types of bicycles. Google images is ripe with these images for each type of bike.

I want to publish a blog post about my project, and put my code (including scraper) on github but not upload the image files anywhere. I might put up a (free) endpoint hosting my resulting classifier if it works.

Questions:

1. Are all images on google images fair game for training data or do I have to limit it to images “labelled for reuse”?
2. Do I have to cite the images I use as training data?
3. I’ve read about “fair use”, how does that figure in here?

Thanks, and sorry if this has been covered elsewhere

Recent work from a group at Google Brain.

Abstract

Much of model-based reinforcement learning involves learning a model of an agent’s world, and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for agents, every naturally occurring model of the world of which we are aware—e.g., a brain—arose as the byproduct of competing evolutionary pressures for survival, not minimization of a supervised forward-predictive loss via gradient descent. That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances. Crucially, this optimization process need not explicitly be a forward-predictive loss. In this work, we introduce a modification to traditional reinforcement learning which we call observational dropout, whereby we limit the agents ability to observe the real environment at each timestep. In doing so, we can coerce an agent into learning a world model to fill in the observation gaps during reinforcement learning. We show that the emerged world model, while not explicitly trained to predict the future, can help the agent learn key skills required to perform well in its environment.

web article: https://learningtopredict.github.io

arxiv: https://arxiv.org/abs/1910.13038

I get it; He is a terrible and shitty person for stealing, plagiarizing, and profiting off of it. However, it’s starting to turn into TMZ in this subreddit with the childish, cancel culture with zero, productive actions. I come her to read about cool research and everyone’s neat projects that they would love to share. I like when people have questions about a paper, or are wanting feedback on their projects. Can we just ban his videos / content?

Hi, I been trying to learn a bit of machine learning for a project that I’m working in and at the moment I managed to classify text using SVM with sklearn and spacy having some good results, but i want to not only classify the text with svm, I also want it to be classified based on a list of keywords that I have. For example: If the sentence has the word fast or seconds I would like it to be classified as performance.

I’m really new to machine learning and I would really appreciate any advice.

Hello Reddit! We reviewed state-of-the-art Adversarial Attacks as well as Defenses against them in our paper. We cover images, graphs and text domains.

I eagerly look forward to your comments!

Paper: https://arxiv.org/abs/1909.08072

Hello everyone I have a project for my AI machine learning course that will be based on city data, namely Vancouver. The below link are the data sets that our project can be based upon. We are free to use outside data sets but must relate it to our city.

https://opendata.vancouver.ca/explore

I would really appreciate any guidance or input. We’re having a difficult time coming up with ideas, the only ones we have come up with are predicting of house property, bike theft, general crime prediction all of which would combine features from other data sets.

Thank you for reading my post!

I am working with a database that is spread across 7 tables and for ML stuff I need to join them together. However, as some of these rows are not sampled as frequently as others, this leaves a lot of nulls for some features. I want to interpolate these values but I’m not sure the most efficient way to do so. In other words, let’s say I have feature X sampled every 10 ms and feature Y every 1 second, and a third feature Z sampled every 15 seconds. I could store it in the database, but I don’t know if allowing that kind of storage capacity is feasible for us. Alternatively, I could calculate it for each row when I get batches for training, but I’m afraid that will become a bottleneck depending on how fast the interpolation is. Is there some obvious way of interpolating this efficiently that I’m not thinking of that will allow me to save on memory space?

Hi everyone,

I have a project where I have to do a quick POC of speech recognition in noisy environment and multispeaker setting. However, I am having a hard time finding a pretrained model with language model rescoring (or any other decoding helper). Any repo or links are welcome!

For preliminary research work, should one submit their paper to an average conference (e.g. ACCV, IJCNN, WACV, BMVC) or to a workshop organised in conjunction with a highly reputed conference (e.g., ICML/NeurIPS/ICLR/AAAI workshop)? What are the pros and cons for each option?

Related links:

• What value do top conference workshop papers (say CVPR, ICCV) add for a PhD admission in Computer Vision at top universities? How does it compare to less prestigious conferences (WACV, BMVC)? What impact does non top publications have?

• How valuable is to have a workshop paper for graduate admission?

• Is the paper in the NIPS ML4H (machine learning for health) workshop considered a good publication? How do you compare the paper in the NIPS ML4H workshop with a regular paper in a normal conference in computer science?

• Should one send their paper to an average conference or to a workshop held in conjunction with a highly reputed conference?