So I’m going to work on a research project and was asked to look at the state of the art works on real-time gesture recognition. What are some of the latest works that achieve state of the art performance?

I want to do a machine learning project to gain insight into and then perhaps even be able to contribute ideas to the area of addiction treatment, something I think is extremely important for society. In order to begin, I have been scouring the Internet for places where I can gather data for building datasets, and maybe even existing datasets I’ll be able to utilize.

So, in this spirit, I was wondering if anyone else has any suggestions or knowledge of where I can gather data that has to do with addiction treatment, or maybe even existing datasets about the subject? Please let me know if you do – it’d be immensely helpful!

P.S. – If you are interested in using some of your free time to help me out with the project, feel free to message me. Especially now, during the data sourcing part of the project, it’s the best part! (Lmao I’m obviously kidding I hate this part, but your help would actually be seriously awesome)

Following is my new blog post. This time I played a bit with the new beta version of TF and implemented a simple model where y is the sum of K gaussians which parameters are learned.

http://zachmoshe.com/2019/08/01/sum-of-gaussians-layer-with-keras-2.0.html

Hello Everyone,

I am looking for suggestions to organize my related work research. It will be great if the method (or system) can give the ability to organize the related work in a web-based tool, where I can keep pdf’s , annotate them online (as well as offline), add comments, write summaries next to them. It will be great if I can share these features with my collaborators as well.

I had been trying to use Trello and Github’s Project Management Tool ( along with “Issues” for comments) for this.

It will be helpful if others can share how they organize their research.

Thanks,

Anurag Koul

Can someone help me understand the differences between LDA (Latent Dirichlet Allocation) and LSA ( Latent Sematic Analysis)? What are the scenarios where both can be used? When do we choose one over the other?

Blog post here

Code here

I recently rewrote node2vec, which took a severely long time to generate random walks on a graph, by representing the graph as a CSR sparse matrix, and operating directly on the sparse matrix’s data arrays.

The result is a speedup from 30 hours to 3 minutes for a small sized graph (nodes and edges in the hundreds of thousands).

This raises bigger questions about graph representation for graph analytics — representing graphs as sparse matrices prevents node insertion, but makes operations much more efficient (though admitedly harder to write). More importantly, we can hold fairly huge graphs in RAM because the data usage is so lean.

If we’re analyzing graphs, we don’t care so much about adding nodes, so I think the future of graph analytics is in CSR representation.

Nowadays, talking to conversational agents is becoming a daily routine, and it is crucial for dialogue systems to generate responses as human-like as possible. As one of the main aspects, primary attention should be given to providing emotionally aware responses to users. In this article, we are going to describe the recurrent neural network architecture for emotion detection in textual conversations, that participated in SemEval-2019 Task 3 “EmoContext”, that is, an annual workshop on semantic evaluation. The task objective is to classify emotion (i.e. happy, sad, angry, and others) in a 3-turn conversational data set.

The rest of the article is organized as follows. Section 1 gives a brief overview of the EmoContext task and the provided data. Sections 2 and 3 focus on the texts pre-processing and word embeddings, consequently. In section 4, we described the architecture of the LSTM model used in our submission. In conclusion, the final performance of our system and the source code are presented. The model is implemented in Python using Keras library.

https://habr.com/en/company/mailru/blog/439850/

How do I find the corresponding frame of, let’s say, Pacman, starting from a RAM state ?

Very interesting article and paper: https://deepmind.com/blog/predicting-patient-deterioration/